{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a VRA modification of state's Home Districts to accommodate VRA districts\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS VANILLA HD + a forced VRA district  #from WI 7/1/24\n",
    "print(\"This is a VRA modification of state's Home Districts to accommodate VRA districts\")\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:  #stop looping if no new neighbors found on previous loop\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def avoidedEnclave(UNITLIST, UNITNBRS, ADDLIST,MAXLOOPS=15):  #20 is arbitrary, but does suppress slender chains\n",
    "    \"\"\"\n",
    "    This method (new for WI 5-23-24) is designed as a shorter check than enclaveCheck for legitimacy of adding ADDLIST to UNITLIST\n",
    "    It tries to find contiguity among all ADDLIST's neighbors that won't be in the UNITLIST after the add.\n",
    "    It does this in successive loops, growing from one non-UNITLIST neighbor in loops to all its non-UL neighbors\n",
    "    If success before MAXLOOPS, it returns True (i.e. this list addition avoided forming an enclave), else it returns False\n",
    "        Note that this method will not usually allow a UNITLIST that doesn't currently touch map border to start touching it\n",
    "        (too many loops to go all the way around the UNITLIST to find the complement's connection)\n",
    "    \"\"\"   \n",
    "    willBeInUnitSet = set(UNITLIST+ADDLIST)\n",
    "    nbrsOfADDLIST = set()\n",
    "    for U in ADDLIST:\n",
    "        nbrsOfADDLIST = nbrsOfADDLIST.union(set(UNITNBRS[U]))  #these are the neighbors of the units we plan to add to UNITLIST\n",
    "    nbrsOfADDLIST = nbrsOfADDLIST.difference(willBeInUnitSet)\n",
    "    if len(nbrsOfADDLIST) == 0:\n",
    "        raise Exception(\"ERROR! Attempted to add list\",ADDLIST,\"but it should have been flagged previously as an enclave\")\n",
    "    firstNo = next(iter(nbrsOfADDLIST))  #pick one of these non-UL neighbors as a starter\n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfADDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnect = False\n",
    "    if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still DIRECTLY connect after the add\n",
    "        stillConnect = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnect:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.difference(willBeInUnitSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfADDLIST)  #running list of complement-connectable non-UL neighbors of ADDLIST\n",
    "        if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still connect after the add\n",
    "            stillConnect = True\n",
    "    return stillConnect\n",
    "              \n",
    "\n",
    "def avoidedIsland(UNITLIST, UNITNBRS, SHEDLIST,MAXLOOPS=15):\n",
    "    \"\"\"\n",
    "    analogous to avoidedEnclave but for shedding a list from UNITLIST; we look to connect in-UNITLIST neighbors after the proposed shed\n",
    "    \"\"\"\n",
    "    shrunkSet = set(UNITLIST).difference(set(SHEDLIST))  #shorthand for the proposed unitlist after shedding\n",
    "    nbrsOfSHEDLIST = set()\n",
    "    for U in SHEDLIST:\n",
    "        nbrsOfSHEDLIST = nbrsOfSHEDLIST.union(set(UNITNBRS[U]))\n",
    "    nbrsOfSHEDLIST = nbrsOfSHEDLIST.intersection(shrunkSet)\n",
    "    if len(nbrsOfSHEDLIST) == 0:\n",
    "        raise Exception(\"ERROR! Attempted to shed list\",SHEDLIST,\"but it was never connected to the rest of the unit list\")\n",
    "    firstNo = next(iter(nbrsOfSHEDLIST))  #pick one of these still-in-UL neighbors as a starter   \n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfSHEDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnects = False\n",
    "    if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #ALL in-UNITLIST neighbors of the shedlist still DIRECTLY connect after the shed\n",
    "        stillConnects = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnects:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.intersection(shrunkSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfSHEDLIST)\n",
    "        if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #all in-UNITLIST neighbors still connect after the shed\n",
    "            stillConnects = True\n",
    "    return stillConnects\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave\n",
    "\n",
    "def isRookAdj(geo1, geo2):  #intersection with rook (not queen) adjacency\n",
    "    isRookAdj = False\n",
    "    if geo1.intersects(geo2):\n",
    "        if (geo1.intersection(geo2)).geom_type != Point(0,0).geom_type:\n",
    "            isRookAdj = True\n",
    "    return isRookAdj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  WI\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use wi_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. WI has 5893718 peeps and is divided into 7059 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 72 counties in WI .  I will assign them county Nos 0 - 71\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']\n",
    "tractWhiteVAP = tractPopFile['P0030003']  #this is within 2% of VEST's vap_white\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 2 unpopulated coastal tracts.\n",
      "Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.\")\n",
    "if len(skipList) > 0:\n",
    "    print(\"Lets plot them and their parent counties\")\n",
    "    for t in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],6)\n",
    "    plotPoly(origMAP,0.2)\n",
    "    for c in range(nCounties):\n",
    "        if isAlteredCounty[c]:\n",
    "            plotPoly(countyGeom[c])\n",
    "    plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 2 units will be axed\n",
      "here is the proposed skipList [3852, 3505]\n",
      "here is the final skipList [3852, 3505]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [146, 3084] #...  #alter here if needed - other two for WA are not worth cutting out\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 53\n",
      "removing coastal units from countyNo 64\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.71263\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbc5333c-67fc-4eaa-94a3-d78003e5f167",
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleted populated-island code; not needed for WI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For WI my input file says to cut out 0 districts out of 8\n",
      "There were no cut districts in WI\n"
     ]
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "\n",
    "CCBlistString = trimDF[\"CCB counties\"][DFrow]\n",
    "CCBlist = ast.literal_eval(CCBlistString)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 5893718.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 2 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 8 rather than original 8\n",
      "Each district will be drawn to house 736714.75 = 1/ 8 of non-cutout state pop 5893718.0 vs original= 5893718.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "here is the WI map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 8 #11  #reduce for TX\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 5 #7 #reduce for TX\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91bf5db4-45db-41ba-b790-64b3e46bdea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing pops in NE coastal county 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing pops in NE coastal county 14\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing pops in NE coastal county 30\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cList = [4,14,30]\n",
    "for c in cList:\n",
    "    print(\"visualizing pops in NE coastal county\",c)\n",
    "    for t in countyTractList[c]: #making sure I separate counties 4 and 14 - is the pop concentrated?\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(tractPop[t],tractGeom[t],8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "the max LAT-scaled distance between counties is 5.00044\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 5.00044  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "collapsedCountyList = list()  #VA has independent cities that we will reassign to parent counties\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "398fbc6d-3b05-4c1f-87cb-72d11cdd98bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "useCCBlist = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use the cutNClip csv list of corner-county bundles\n",
      "Our final pops and fused-county lists are...\n",
      "13737.0 [18, 20]\n",
      "169151.0 [29]\n",
      "171003.0 [15, 1, 3, 6, 65, 57, 25, 50, 63]\n",
      "92501.0 [14, 30, 37]\n",
      "51938.0 [21]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts  #this block modified from VRA-HD-MO\n",
    "if useCCBlist:\n",
    "    print(\"We will use the cutNClip csv list of corner-county bundles\")\n",
    "    nCCBs = len(CCBlist)\n",
    "    CCBpop, CCBgeom = [0.]*nCCBs, [countyGeom[L[0]] for L in CCBlist]\n",
    "    for i, L in enumerate(CCBlist):\n",
    "        for cc in L:\n",
    "            CCBpop[i] += countyPop[cc]\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[cc])\n",
    "    passThruList, rejectList = list(), list()\n",
    "else:\n",
    "    maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "    maxCCBpop = maxCCBratio * aDP\n",
    "    print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "    CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "    nFuses = 0\n",
    "    for c in set(has1neighborCs).difference(set(collapsedCountyList)):\n",
    "        print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "        if countyPop[c] > maxCCBpop:\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(c,countyGeom[c])\n",
    "            nc = neighborCountyList[c][0]\n",
    "            plotPoly(countyGeom[nc],0.5)\n",
    "            plotPoly(MAP,0.2)\n",
    "            print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "            print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "            fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "            if fuseChoice == 0:\n",
    "                keepUnfused = True\n",
    "                break\n",
    "            elif int(fuseChoice) == 1:\n",
    "                CCBlist.append([c])\n",
    "                CCBpop.append(countyPop[c])  \n",
    "                hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "            elif int(fuseChoice) == 2:\n",
    "                CCBlist.append([c,nc ] )\n",
    "                CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "                hasFewNeighborCs.append(c)\n",
    "                passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "        else:\n",
    "            hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "            \n",
    "    for c in set(hasFewNeighborCs).difference(set(collapsedCountyList)):\n",
    "        print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "        if countyPop[c] < maxCCBpop :\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])\n",
    "            CCBno = CCBlist.index([c])\n",
    "        if c in passThruList:\n",
    "            CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "        if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "            CCBstarter = c\n",
    "            canAdd = True\n",
    "            while canAdd:\n",
    "                nbrSet = set()\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "                nPop = [countyPop[cc] for cc in nbrSet]\n",
    "                nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "                idx = np.argsort(nDist)\n",
    "                nbrList, newList = list(nbrSet), list()\n",
    "                for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                    cc = nbrList[idx[i]]\n",
    "                    if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                        newList.append(cc)\n",
    "                        CCBlist[CCBno].append(cc)\n",
    "                        CCBpop[CCBno] += countyPop[cc]\n",
    "                        #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "                if newList == list():  #no eligible neighbor counties found; stop\n",
    "                    canAdd = False\n",
    "                    for cc in CCBlist[CCBno]:\n",
    "                        plotPoly(countyGeom[cc])\n",
    "                        plotCenter(CCBno,countyGeom[cc])\n",
    "    plotPoly(MAP,0.2)\n",
    "    plt.show()\n",
    "                        \n",
    "    print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "    plotPoly(MAP,0.15)\n",
    "    for L in CCBlist:\n",
    "        print(CCBlist.index(L),\" \",L)\n",
    "        for c in L:\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(c,countyGeom[c])\n",
    "            for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "                plotPoly(countyGeom[cc],0.4)\n",
    "                plotCenter(cc,countyGeom[cc],8)\n",
    "    plt.show()\n",
    "    rejectList = list()\n",
    "    print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "    print(\"You may suggest [ [18,20], [29], [15,1,3,6,65,57,25,50,63],[14,30, 37], [21] ] \")\n",
    "\n",
    "    for i,L in enumerate(CCBlist):\n",
    "        if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "            print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "            isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "            if not (isOK == 1 or isOK == str(1)) :\n",
    "                if isOK == 0 or isOK == str(0) :\n",
    "                    rejectList.append(L)\n",
    "                else:\n",
    "                    notGood = True\n",
    "                    while notGood:       \n",
    "                        Lnew = ast.literal_eval(isOK)        \n",
    "                        if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                            notGood = False\n",
    "                        else:\n",
    "                            print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                    CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                    CCBlist[i] = Lnew.copy()\n",
    "    for L in rejectList:\n",
    "        del CCBpop[ CCBlist.index(L)]\n",
    "        del CCBlist[CCBlist.index(L)]\n",
    "    stillAdding = True\n",
    "    while stillAdding:\n",
    "        addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "        if int(addMore) != 1:\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                print(\"OK, we'll stop adding corner clusters\")\n",
    "                stillAdding = False\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                        for L in CCBlist:\n",
    "                            if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                                notGood = True\n",
    "                                print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                                isOK = input(\"Try again here \")\n",
    "                        if notGood == False:\n",
    "                            CCBlist.append(Lnew)\n",
    "                            CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "    print(\"Our final pops and fused-county lists are...\")\n",
    "    allFusedCounties = list()\n",
    "    CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "    CCBcp = list()\n",
    "    for i, L in enumerate(CCBlist):\n",
    "        CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "        print(CCBpop[i],L)\n",
    "        pops, CPs = list(), list()\n",
    "        for c in L:\n",
    "            if c == L[0]:\n",
    "                CCBgeom[i] = countyGeom[c]\n",
    "            else:\n",
    "                CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "            allFusedCounties.append(c)\n",
    "            pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "            CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(i,countyGeom[c])\n",
    "    plotPoly(MAP,0.2)\n",
    "    plt.show()\n",
    "    \n",
    "    for i,L in enumerate(CCBlist):\n",
    "        if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "            print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "            isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "            if not (isOK == 1 or isOK == str(1)) :\n",
    "                if isOK == 0 or isOK == str(0) :\n",
    "                    rejectList.append(L)\n",
    "                else:\n",
    "                    notGood = True\n",
    "                    while notGood:       \n",
    "                        Lnew = ast.literal_eval(isOK)        \n",
    "                        if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                            notGood = False\n",
    "                        else:\n",
    "                            print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                    CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                    CCBlist[i] = Lnew.copy()\n",
    "    for L in rejectList:\n",
    "        del CCBpop[ CCBlist.index(L)]\n",
    "        del CCBlist[CCBlist.index(L)]\n",
    "    stillAdding = True\n",
    "    while stillAdding:\n",
    "        addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "        if int(addMore) != 1:\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                print(\"OK, we'll stop adding corner clusters\")\n",
    "                stillAdding = False\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                        for L in CCBlist:\n",
    "                            if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                                notGood = True\n",
    "                                print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                                isOK = input(\"Try again here \")\n",
    "                        if notGood == False:\n",
    "                            CCBlist.append(Lnew)\n",
    "                            CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 14734\n",
      "We defined a total of 4 unit (but not corner-cluster) counties in the map\n",
      "excluding the cut-out districts and collapsed counties\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in set(uncutCountyList).difference(set(collapsedCountyList)):\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map\")\n",
    "print(\"excluding the cut-out districts and collapsed counties\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "591 fragmented VTDs out of 7059\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "def89086-e45b-4617-8194-e3c471caff6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New for WI VRA - pre-combine (frag)VTDs to have < 400 units per district\n",
      "... after we assign unit nos to the unit counties\n",
      "Now identifying discontig precincts that we will exclude from fusing to reduce units / county\n",
      "I split up 190 fragmented VTDs, leaving 401 to be fused in a later block\n",
      "If we don't combine some VTDs, we would have 6695 total units, or 836.875 per district\n"
     ]
    }
   ],
   "source": [
    "print(\"New for WI VRA - pre-combine (frag)VTDs to have < 400 units per district\")\n",
    "# ... below code originally in block below, but moved here\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, borderUnits = list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"... after we assign unit nos to the unit counties\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "    countyUnitList[c] = [c+0.5]\n",
    "print(\"Now identifying discontig precincts that we will exclude from fusing to reduce units / county\")\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDs = [countyTractList[c].copy() for c in range(nCounties)]\n",
    "cantFuseList, mustFuseList, fuseBuddyList = list(), list(), list()\n",
    "nonunitCounties = list()\n",
    "for c in range(nCounties):\n",
    "    if c not in allFusedCounties + unitCounties:\n",
    "        nonunitCounties.append(c)\n",
    "for c in nonunitCounties:\n",
    "    for v in countyTractList[c]:\n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :   \n",
    "            cantFuse = True  #a multipoly vtd-based unit.  For simplicity, do not buddy-fuse these\n",
    "            if v not in fuseBuddyList: #avoid reciprocal fusing; too complicated\n",
    "                for vv in countyTractList[c]:\n",
    "                    if v != vv and (vtdGeom[v].union(vtdGeom[vv]) ).geom_type == dummyPoly.geom_type :                    \n",
    "                        mustFuseList.append(v)  #fusing these two cures the split VTD, so fuse instead\n",
    "                        fuseBuddyList.append(vv)\n",
    "                        cantFuse = False\n",
    "                        break\n",
    "            if cantFuse: #break VTD into its fragments\n",
    "                cantFuseList.append(v)                \n",
    "                nFragmentedVTDs +=1\n",
    "                fragmentedVTDs.append(v)\n",
    "                geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "                for i, geo in enumerate(geos):\n",
    "                    if i == 0:\n",
    "                        fragVTDgeom[v] = geo\n",
    "                        fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                    else:  #create a new VTDfrag\n",
    "                        fNo = len(fragVTDgeom)\n",
    "                        fragVTDgeom.append(geo)\n",
    "                        fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                        parentVTDno.append(v)\n",
    "                        countyFragVTDs[c].append(fNo)\n",
    "                        VTDchildren[v].append(fNo)\n",
    "                        nVTDneighbors.append(0)\n",
    "                        lastVTDneighbor.append(-999)\n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs, leaving\",len(mustFuseList),\"to be fused in a later block\")\n",
    "nOrigUnits = len(fragVTDgeom) + len(unitCounties) + nCCBs\n",
    "nOrigUnits -= np.sum([len(countyFragVTDs[c]) for c in allFusedCounties + unitCounties])\n",
    "nOrigUnitsPerDistrict = float(nOrigUnits)/nDistricts\n",
    "print(\"If we don't combine some VTDs, we would have\",nOrigUnits,\"total units, or\",nOrigUnitsPerDistrict,\"per district\")\n",
    "\n",
    "fusedGeoms = [ [fragVTDgeom[vv] for vv in countyFragVTDs[c] ] for c in range(nCounties) ]\n",
    "fusedPops =  [ [fragVTDpop[vv]  for vv in countyFragVTDs[c] ] for c in range(nCounties) ]\n",
    "isWiped =    [ [False           for vv in countyFragVTDs[c] ] for c in range(nCounties) ] #surrounded or fused\n",
    "for c in unitCounties:\n",
    "    fusedGeoms[c] = [ countyGeom[c] ]  #these three won't be accessed, but just in case ...\n",
    "    fusedPops[c]  = [ countyPop[c]  ]\n",
    "    isWiped[c]    = [ False         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "3af493e2-9b9b-42aa-9034-b164271f187e",
   "metadata": {},
   "outputs": [],
   "source": [
    "debug3 = False\n",
    "VRAcounties = [VRAc]  #to keep the VRA core clean, do not fuse vtds in Milwaukee county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "874b0de3-34a6-4e63-bf6d-1d26dabbf8a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now apply all FORCED fuses (multigeom VTDs made whole by fusing w another single VTD)\n",
      "20654 county 0 pops before fusing its forced-fuse list\n",
      "20654 county 0 pops after the fusing operation\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "To speed computation, enter max number of units per district; e.g. 400 400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created temp nbr list for county 0 with 43 11 original and target UPC\n",
      "created temp nbr list for county 10 with 87 31 original and target UPC\n",
      "12 370 c,NFU. target V index will now be 553 which refers to v 7168\n",
      "12 360 c,NFU. target V index will now be 9 which refers to v 3077\n",
      "12 350 c,NFU. target V index will now be 26 which refers to v 3101\n",
      "12 340 c,NFU. target V index will now be 38 which refers to v 3113\n",
      "12 330 c,NFU. target V index will now be 42 which refers to v 3117\n",
      "12 330 c,NFU. target V index will now be 54 which refers to v 3129\n",
      "12 320 c,NFU. target V index will now be 42 which refers to v 3117\n",
      "12 320 c,NFU. target V index will now be 76 which refers to v 3151\n",
      "12 310 c,NFU. target V index will now be 42 which refers to v 3117\n",
      "12 310 c,NFU. target V index will now be 87 which refers to v 3162\n",
      "17 60 c,NFU. target V index will now be 2 which refers to v 744\n",
      "26 40 c,NFU. target V index will now be 33 which refers to v 1229\n",
      "26 30 c,NFU. target V index will now be 45 which refers to v 3047\n",
      "26 30 c,NFU. target V index will now be 46 which refers to v 3048\n",
      "26 20 c,NFU. target V index will now be 45 which refers to v 3047\n",
      "26 20 c,NFU. target V index will now be 8 which refers to v 721\n",
      "31 110 c,NFU. target V index will now be 132 which refers to v 7267\n",
      "31 100 c,NFU. target V index will now be 4 which refers to v 1400\n",
      "31 90 c,NFU. target V index will now be 18 which refers to v 1464\n",
      "31 80 c,NFU. target V index will now be 29 which refers to v 3788\n",
      "31 70 c,NFU. target V index will now be 41 which refers to v 4351\n",
      "created temp nbr list for county 35 with 93 44 original and target UPC\n",
      "created temp nbr list for county 45 with 96 49 original and target UPC\n",
      "created temp nbr list for county 55 with 134 50 original and target UPC\n",
      "created temp nbr list for county 60 with 36 10 original and target UPC\n",
      "created temp nbr list for county 70 with 282 93 original and target UPC\n"
     ]
    }
   ],
   "source": [
    "print(\"Now apply all FORCED fuses (multigeom VTDs made whole by fusing w another single VTD)\")\n",
    "print(np.sum(fusedPops[0]),\"county 0 pops before fusing its forced-fuse list\" )\n",
    "for i, v in enumerate(mustFuseList):\n",
    "    vv = fuseBuddyList[i]\n",
    "    c = countyNo[v]\n",
    "    vIdx, vvIdx = countyFragVTDs[c].index(v), countyFragVTDs[c].index(vv)\n",
    "    surroundedVTDs.append(v)\n",
    "    surrounders.append(vv)\n",
    "    fusedPops[c][vvIdx] += fragVTDpop[v]\n",
    "    fusedGeoms[c][vvIdx] = fusedGeoms[c][vvIdx].union(fusedGeoms[c][vIdx])\n",
    "    fusedPops[c][vIdx], isWiped[c][vIdx] = 0, True\n",
    "print(np.sum(fusedPops[0]), \"county 0 pops after the fusing operation\" )\n",
    "    \n",
    "# and now the additional fuses to reduce units / district.  Fuse within counties only\n",
    "maxUnitsPerDistrict = int(input(\"To speed computation, enter max number of units per district; e.g. 400\"))\n",
    "for c in nonunitCounties:    \n",
    "    if c not in VRAcounties: #keeping these unfused to get 1:1 coreVTDs <--> coreUset\n",
    "        targetUnitsPerCounty = countyPop[c] / aDP * maxUnitsPerDistrict\n",
    "        nFusedUnits, nCFVs = 0, len(countyFragVTDs[c])\n",
    "        for wiped in isWiped[c]:\n",
    "            if not wiped:\n",
    "                nFusedUnits +=1\n",
    "        if nFusedUnits <= targetUnitsPerCounty:\n",
    "            print(\"county\",c,\"does not need fusing; only has\",nFusedUnits,\"vs max\",r5(targetUnitsPerCounty) )\n",
    "        else:  #create neighbor list of VTD frags in this county.  then fuse until county units suff'ly reduced\n",
    "            cNbrs = [list() for geo in fusedGeoms[c] ]  #neighbor list, excluding above surroundees\n",
    "            for ii, geo in enumerate(fusedGeoms[c]):\n",
    "                if not isWiped[c][ii]:\n",
    "                    for iii, nGeo in enumerate(fusedGeoms[c]):\n",
    "                        if not isWiped[c][iii] and ii != iii:\n",
    "                            if isRookAdj(geo,nGeo):\n",
    "                                cNbrs[ii].append(iii)\n",
    "                                cNbrs[iii].append(ii)\n",
    "            if c%5 == 0:\n",
    "                print(\"created temp nbr list for county\",c,\"with\",nFusedUnits,\n",
    "                      int(targetUnitsPerCounty),\"original and target UPC\")\n",
    "            while nFusedUnits > targetUnitsPerCounty + 1: #find smallest nonwiped frag, fuse w/smallest extant neighbor\n",
    "                vIdxList, vPops = list(), list()          \n",
    "                for jj, wiped in enumerate(isWiped[c]):\n",
    "                    if not wiped:\n",
    "                        vIdxList.append(jj)\n",
    "                        vPops.append(fusedPops[c][jj])\n",
    "                vOrder = np.argsort(vPops)\n",
    "                indexC = 0\n",
    "                foundNeighborlyV = False\n",
    "                vListIndex = vPops.index(np.min(vPops))\n",
    "                vIdx = vIdxList[ vListIndex ]\n",
    "                while not foundNeighborlyV:  #step from lowest pop to highest here to look for suitable fuse victim\n",
    "                    #vIdx = vIdxList[vOrder[indexC]]\n",
    "                    nList, nPops = list(), list()\n",
    "                    for nIdx in cNbrs[vIdx]:\n",
    "                        if not isWiped[c][nIdx] and nIdx != vIdx: #just in case\n",
    "                            nList.append(nIdx)\n",
    "                            nPops.append(fusedPops[c][nIdx] )\n",
    "                        else:  #this neighbor has been surrounded/fused.  Point to its surrounder as a fuse candidate\n",
    "                            surrFV = surrounders[ surroundedVTDs.index(countyFragVTDs[c][nIdx]) ]\n",
    "                            surrIdx = countyFragVTDs[c].index(surrFV)\n",
    "                            if surrIdx != vIdx :\n",
    "                                nList.append(surrIdx)\n",
    "                                nPops.append(fusedPops[c][surrIdx])\n",
    "                    if len(nList) == 0:\n",
    "                        #print(\"eschewing low-pop v\",countyFragVTDs[c][vIdx],\"in county\",c,\"cant be fused now; retrying...\")                      \n",
    "                        vListIndex = int(  (vListIndex+1)%len(vIdxList)  )  #KISS, just cycle through\n",
    "                        vIdx = vIdxList[ vListIndex ]\n",
    "                        #indexC += 1\n",
    "                        if nFusedUnits%10 == 0:\n",
    "                            print(c,nFusedUnits,\"c,NFU. target V index will now be\",vIdx,\"which refers to v\",countyFragVTDs[c][vIdx])\n",
    "                        #print(vOrder)\n",
    "                        #pause = input(\"ready to continue?\")\n",
    "                    else:\n",
    "                        foundNeighborlyV = True                        \n",
    "                        vvIdx = nList[nPops.index(np.min(nPops ) ) ]   #... and fuse to smallest neighbor \n",
    "                        fusedV, masterV = countyFragVTDs[c][vIdx], countyFragVTDs[c][vvIdx]\n",
    "                        surroundedVTDs.append(fusedV)\n",
    "                        surrounders.append(   masterV)\n",
    "                        for ijk, v in enumerate(surroundedVTDs):\n",
    "                            if surrounders[ijk] == fusedV:\n",
    "                                surrounders[ijk] = masterV\n",
    "                                if debug3:\n",
    "                                    print(\"surrounder for\",v,\"changed from\",fusedV,\"to\",masterV,\"= indices ..\",vIdx, vvIdx)\n",
    "                        fusedPops[c][vvIdx] += fusedPops[c][vIdx]\n",
    "                        fusedPops[c][vIdx ] = 0\n",
    "                        isWiped[c][vIdx] = True\n",
    "                        nFusedUnits -=1\n",
    "                        if debug3:\n",
    "                            print(\"   \",c,nFusedUnits, vIdx, vvIdx,\n",
    "                                  \"c,nFusedUnits, wiped index, receiving index\",int(np.sum(fusedPops[c])))\n",
    "    # This county done. Now update the fused geometries for the surrounders in this county\n",
    "        for jj, vf in enumerate(countyFragVTDs[c]):\n",
    "            if vf in surrounders:\n",
    "                for ii,capturedVF in enumerate(surroundedVTDs):\n",
    "                    if surrounders[ii] == vf:\n",
    "                        vIdx = countyFragVTDs[c].index(capturedVF)\n",
    "                        fusedGeoms[c][jj] = fusedGeoms[c][jj].union(fusedGeoms[c][vIdx])\n",
    "                        \n",
    "                    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "81420c41-3a57-467c-be3b-d2372efa47d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data integrity after fuse operation in county 70\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "areasum = 0.16833849120650082 vs county area 0.16833849120650093\n",
      "popsum = 170906 vs county total pop 171730.0\n",
      "171730.0\n"
     ]
    }
   ],
   "source": [
    "c = 70\n",
    "print(\"data integrity after fuse operation in county\",c)\n",
    "cPOP = 0.\n",
    "areaSum = 0.\n",
    "for i, geo in enumerate(fusedGeoms[c]):\n",
    "    if not isWiped[c][i]:\n",
    "        plotPoly(geo)\n",
    "        plotCenter(\"N\",geo)\n",
    "        areaSum += geo.area\n",
    "        cPOP += fusedPops[c][i]\n",
    "    else:\n",
    "        plotPoly(geo,0.3)\n",
    "plt.show()\n",
    "print(\"areasum =\",areaSum,\"vs county area\",countyGeom[c].area)\n",
    "print(\"popsum =\",int(cPOP),\"vs county total pop\",countyPop[c])\n",
    "print(np.sum(fusedPops[c]))\n",
    "for v in countyFragVTDs[c]:\n",
    "    if v in surroundedVTDs:\n",
    "        pass\n",
    "        #print(v,\"is surrounded by\",surrounders[surroundedVTDs.index(v)] )\n",
    "    surrL = list()\n",
    "    if v in surrounders:\n",
    "        for ii, vv in enumerate(surroundedVTDs):\n",
    "            if surrounders[ii] == v:\n",
    "                surrL.append(vv)\n",
    "        #print(\"   \",v,\"surrounds fragVTDs\",surrL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "35d74720-ca93-4a28-8660-79303df5f834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, geo in enumerate(fusedGeoms[c]):\n",
    "    if not isWiped[c][i]:\n",
    "        plotPoly(geo.buffer(-0.001))\n",
    "        plotCenter(\"i\",geo)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "9f15583c-4809-44fd-ae6b-e01a3c74bc6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: cPops vs county Pop.  Below = sum area for fused Geoms / county area - 1.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in nonunitCounties:\n",
    "    plt.scatter(c,np.sum(fusedPops[c])/countyPop[c])\n",
    "plt.show()\n",
    "print(\"above: cPops vs county Pop.  Below = sum area for fused Geoms / county area - 1.\")\n",
    "for c in nonunitCounties:\n",
    "    liveList = list()\n",
    "    for i in range(len(isWiped[c])):\n",
    "        if not isWiped[c][i]:\n",
    "            liveList.append(i)\n",
    "    plt.scatter(c,np.sum([fusedGeoms[c][i].area for i in liveList]) / countyGeom[c].area - 1.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now capture all remaining surrounds, convert to units ...\n",
      "looking for surrounds in county 0\n",
      "looking for surrounds in county 10\n",
      "looking for surrounds in county 40\n",
      "looking for surrounds in county 60\n",
      "looking for surrounds in county 70\n",
      "special for WI , I believe that [3453] are surrounded by vtd (fragment) 2910 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After surround checks, we appear to have 2975 building blocks defined. We captured 3718 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "print(\"now capture all remaining surrounds, convert to units ...\")\n",
    "\n",
    "debugV = -9999 #update to look closely at a vtdFrag\n",
    "for c in nonunitCounties:\n",
    "    if c % 10 == 0:\n",
    "        print(\"looking for surrounds in county\",c)\n",
    "    for kk, v in enumerate(countyFragVTDs[c]):\n",
    "        if not isWiped[c][kk]:\n",
    "            for nn, vv in enumerate(countyFragVTDs[c]) :\n",
    "                if not isWiped[c][nn] and vv != v :\n",
    "                    if isRookAdj(fusedGeoms[c][kk], fusedGeoms[c][nn] ) :\n",
    "                        nVTDneighbors[v] +=1\n",
    "                        lastVTDneighbor[v] = vv\n",
    "\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if isRookAdj(fusedGeoms[c][kk], countyGeom[cc]):\n",
    "                        if cc in nonunitCounties :\n",
    "                            for jj,vv in enumerate(countyFragVTDs[cc]) :\n",
    "                                if isRookAdj(fusedGeoms[c][kk], fusedGeoms[cc][jj] ):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fusedGeoms[c][kk],2)\n",
    "                                plotCenter(\"iso\",fusedGeoms[c][kk])\n",
    "                                plotPoly(countyGeom[c])\n",
    "                                plotPoly(countyGeom[cc],0.2)\n",
    "                                plotCenter(cc, countyGeom[cc])\n",
    "                                plt.show()\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                if STATE == \"TN\":  #for TN, we tolerate some discontiguous counties\n",
    "                                    print(\"vtd fragment\",v,\"with no neighbors in its county\",countyNo[parentVTDno[v]],\n",
    "                                          \"borders a unit or fused county\",cc)\n",
    "                                else:\n",
    "                                    raise Exception(\"ERROR! Neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "        if v == debugV:\n",
    "            print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for c in nonunitCounties:\n",
    "        for kk,v in enumerate(countyFragVTDs[c]):\n",
    "            if nVTDneighbors[v] == 1 and not isWiped[c][kk]:  #2nd condition should never trigger\n",
    "                vv = lastVTDneighbor[v]\n",
    "                if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                    for sNo, s in enumerate(surroundedVTDs):\n",
    "                        if surrounders[sNo] == v:\n",
    "                            surrounders[sNo] = vv\n",
    "                surroundedVTDs.append(v)\n",
    "                surrounders.append(vv)\n",
    "                surrK = countyFragVTDs[c].index(vv)\n",
    "                nVTDneighbors[vv]  -=1\n",
    "                nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                fusedPops[c][surrK] += fusedPops[c][kk]\n",
    "                fusedPops[c][kk] = 0\n",
    "                fusedGeoms[c][surrK] = fusedGeoms[c][surrK].union(fusedGeoms[c][kk])\n",
    "                if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                    for jjkk, vvv in enumerate(countyFragVTDs[c]):\n",
    "                        if vvv not in [v,vv] and not isWiped[c][jjkk]:\n",
    "                            if isRookAdj( fusedGeoms[c][jjkk], fusedGeoms[c][surrK] ) : \n",
    "                                lastVTDneighbor[vv] = vvv\n",
    "                if lastVTDneighbor[vv] == v:\n",
    "                    print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                if loop > 0:\n",
    "                    print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"WI\":  #state-specific manual surround enforcement\n",
    "    specialSurrounded = [3453 ] #[1610, 1611]  #need to redo these numbers after border visualization\n",
    "    specialSurrounder = 2910 #2901\n",
    "    print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "    c = countyNo[parentVTDno[specialSurrounder]]\n",
    "    plotPoly(countyGeom[c], 0.5)\n",
    "    for v in specialSurrounded:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v,fragVTDgeom[v])\n",
    "    plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "    plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "    plt.show()\n",
    "    shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "    if shouldIcombine == 1:\n",
    "        for v in specialSurrounded:\n",
    "            vv = specialSurrounder\n",
    "            if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                for sNo, s in enumerate(surroundedVTDs):\n",
    "                    if surrounders[sNo] == v:\n",
    "                        surrounders[sNo] = vv\n",
    "            surroundedVTDs.append(v)\n",
    "            surrounders.append(vv)\n",
    "            nVTDneighbors[vv] -=1\n",
    "            nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "            fragVTDpop[vv] += fragVTDpop[v]\n",
    "            fragVTDpop[v] = 0\n",
    "\n",
    "for c in nonunitCounties:\n",
    "    for jj, v in enumerate(countyFragVTDs[c]):\n",
    "        if nVTDneighbors[v] > 1 and not isWiped[c][jj]:\n",
    "            countyUnitList[c].append( len(unitCP) )  #list of all unitNos in this county\n",
    "            unitCP.append(fusedGeoms[c][jj].centroid)\n",
    "            unitGeom.append(fusedGeoms[c][jj])\n",
    "            unitPop.append(fusedPops[c][jj])   #for now, ratio pop by area, not block decomposition\n",
    "            vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "            allUnits.append(v)\n",
    "           \n",
    "for c in nonunitCounties:\n",
    "    for jj, v in enumerate(countyFragVTDs[c]) :\n",
    "        if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "            print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "        if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "            print(\"WARNING. unsurrounded vtd frag\",v,\"had no found neighbors\")\n",
    " \n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            if STATE != \"WA\" or i * ii != 3 :  #In WA, we don't allow ferry connection of Island to Clallam+Jefferson\n",
    "                unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "                unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Great! all units appear to have multiple neighbors; no checks needed.\n"
     ]
    }
   ],
   "source": [
    "if len(sketchyList) == 0:\n",
    "    print(\"Great! all units appear to have multiple neighbors; no checks needed.\")\n",
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should usually be the case, but exception in VA\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "if STATE == \"WA\":\n",
    "    print(\"adding a border unit between Island and Jefferson Counties, as these are only ferry-connected\")\n",
    "    borderUnits.append(2647)\n",
    "    borderType.append(\"vtd\")\n",
    "\n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 75\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.3)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "        print(\"above is with unit numbers; below is VTDfrag nos\")\n",
    "        for uu in smallPieceList:\n",
    "            vv = allUnits[uu]\n",
    "            plotPoly(unitGeom[uu],0.3)\n",
    "            plotCenter(\"v\"+str(vv),unitGeom[uu],8)\n",
    "        v = allUnits[u]\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(v,unitGeom[u],8)\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"10Jul24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"VRAunitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0\n",
    "nHDs = nTracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "89eb05dc-cb93-4264-ac61-9c07fa7d198e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the next few blocks, we define a VRA core that stays together.  The core is included\n",
      "in all HDs inside a defined envelope, and excluded outside this envelope\n",
      "To determine current minority fractions of these unit-based lists, we must establish unit --> VTD conversn\n",
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "Now, we compute compute minority and total VAP of each unit\n"
     ]
    }
   ],
   "source": [
    "#FOR THE VRA-modified code, below blocks updated from VRA-HD-MO\n",
    "print(\"In the next few blocks, we define a VRA core that stays together.  The core is included\")\n",
    "print(\"in all HDs inside a defined envelope, and excluded outside this envelope\")\n",
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"To determine current minority fractions of these unit-based lists, we must establish unit --> VTD conversn\")\n",
    "noFrag = 0 #int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "\n",
    "print(\"Now, we compute compute minority and total VAP of each unit\")                        \n",
    "unitVAP, unitWhiteVAP = [0.]*nUnits, [0.]*nUnits\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        unitVAP[u]      += tractVAP[v]\n",
    "        unitWhiteVAP[u] += tractWhiteVAP[v]\n",
    "    for i, v in enumerate(unitPartialVTDlist[u]):\n",
    "        unitVAP[u]      += unitVTDfrac[u][i]* tractVAP[v]\n",
    "        unitWhiteVAP[u] += unitVTDfrac[u][i]* tractWhiteVAP[v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f753f2cd-7c78-43d5-88c7-42f68f6b4be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now read in a shapefile of Milwaukee to be kept whole\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"we will now read in a shapefile of Milwaukee to be kept whole\")\n",
    "cityDF = gpd.read_file(\"./state_map_files/Milwaukee.dbf\")\n",
    "cityBoundary = cityDF[\"geometry\"]\n",
    "cityBoundary = cityBoundary.to_crs(tractGeom.crs)[0]\n",
    "VRAc = 40\n",
    "plotPoly(cityBoundary)\n",
    "plotPoly(countyGeom[VRAc])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "f852f86a-850c-4376-8776-e85d4cf877b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and now the vtds in near-Milwaukee counties with vtd mVAP > 0.45\n",
      "this shows that the high BVAP is strongly correlative with Milwaukee boundaries\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now draw a box approximately around the Milwaukee high-minority area\n",
      "the core pop is 0.91508 of a district\n",
      "its mVAP fraction is 0.53028\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mVAPt2 = 0.45\n",
    "print(\"and now the vtds in near-Milwaukee counties with vtd mVAP >\",mVAPt2)\n",
    "print(\"this shows that the high BVAP is strongly correlative with Milwaukee boundaries\")\n",
    "cList = [VRAc]\n",
    "mVpop = 0.\n",
    "localPoly = Polygon([Point(-87.8,43.25),(-88.2,43.25),(-88.2,42.8),(-87.8,42.8)])\n",
    "plotPoly(localPoly,0.2)\n",
    "for c in cList:\n",
    "    #plotPoly(countyGeom[c],2)\n",
    "    plotPoly(cityBoundary)\n",
    "    for t in countyTractList[c]:\n",
    "        if localPoly.contains(tractCP[t]):\n",
    "            if tractWhiteVAP[t] < (1. - mVAPt2)*tractVAP[t] :\n",
    "                plotPoly(tractGeom[t],0.5)\n",
    "                plotCenter(\"B\",tractCP[t])\n",
    "                mVpop += tractPop[t]\n",
    "            else:\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "    for cc in neighborCountyList[c]:\n",
    "        for t in countyTractList[cc]:\n",
    "            if localPoly.contains(tractCP[t]):\n",
    "                if tractWhiteVAP[t] < (1. - mVAPt2)*tractVAP[t] :\n",
    "                    plotPoly(tractGeom[t],0.1)\n",
    "                    plotCenter(\"b\",tractCP[t],15)\n",
    "                else:\n",
    "                    plotCenter(\"w\",tractCP[t],6)\n",
    "plt.show()\n",
    "\n",
    "print(\"Now draw a box approximately around the Milwaukee high-minority area\")\n",
    "ptXs = [-88.06,-88.05,-87.97, -87.88, -87.88,-87.87, -88.01]\n",
    "ptYs =  [43.195,43.105, 42.935,42.93,42.932, 43.085, 43.195]\n",
    "pts = [Point(ptXs[i], ptYs[i]) for i in range(len(ptXs)) ]\n",
    "VRApoly = Polygon(pts)\n",
    "plotPoly(VRApoly)\n",
    "coreVTDs = list()\n",
    "for c in cList:\n",
    "    for t in countyTractList[c]:\n",
    "        if VRApoly.contains(tractCP[t]) or cityBoundary.contains(tractCP[t]):\n",
    "            coreVTDs.append(t)\n",
    "\n",
    "corePop, coreVAP, coreWhiteVAP = 0., 0., 0.\n",
    "for v in coreVTDs:\n",
    "    plotPoly(tractGeom[v],0.2)\n",
    "    plotCenter(\".\",tractCP[v])\n",
    "    corePop += tractPop[v]\n",
    "    coreVAP += tractVAP[v]\n",
    "    coreWhiteVAP += tractWhiteVAP[v]\n",
    "\n",
    "plotPoly(cityBoundary) #countyGeom[VRAc]\n",
    "print(\"the core pop is\",r5(corePop/aDP),\"of a district\")\n",
    "print(\"its mVAP fraction is\",r5(1.-coreWhiteVAP/coreVAP))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "6acd75e7-caed-409a-81ba-6560ba43cc83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now define the envelope of nearby vtds that will include the core in their adjusted HD\n",
      "  Do this by expanding a circle from the centroid of the core until it captures sufficient pop\n",
      "final envelopePop/aDP, corePop/aDP are 0.08748 0.91508\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now define the envelope of nearby vtds that will include the core in their adjusted HD\")\n",
    "print(\"  Do this by expanding a circle from the centroid of the core until it captures sufficient pop\")\n",
    "envelopePop, R, toler = 0., 0.01 * VRApoly.area**0.5, 0.005*aDP\n",
    "coreCP = getHDcp([tractCP[v] for v in coreVTDs],[tractPop[v] for v in coreVTDs],[i for i in range(len(coreVTDs))] )\n",
    "dR = 2.*VRApoly.area**0.5\n",
    "targetPop = aDP - corePop\n",
    "availVTDs = countyTractList[cList[0]]\n",
    "#for c in cList[1:]:  #for this state, only from Milwaukee county, not surrounding\n",
    "#    availVTDs += countyTractList[c]\n",
    "availVTDs = list(set(availVTDs).difference(set(coreVTDs)))\n",
    "while abs(targetPop - envelopePop ) > toler:\n",
    "    R += dR * np.sign(targetPop - envelopePop)\n",
    "    dR *= 0.5\n",
    "    envelopePop, envelopeVs = 0., list()\n",
    "    coreCircle = coreCP.buffer(R)\n",
    "    for v in availVTDs:\n",
    "        if coreCircle.contains(tractCP[v]):\n",
    "            envelopePop += tractPop[v]\n",
    "            envelopeVs.append(v)\n",
    "plotPoly(coreCircle)\n",
    "for v in envelopeVs:\n",
    "    plotPoly(tractGeom[v])\n",
    "for v in coreVTDs:\n",
    "    plotPoly(tractGeom[v],0.2)\n",
    "print(\"final envelopePop/aDP, corePop/aDP are\",r5(envelopePop/aDP), r5(corePop/aDP))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "4e8b1f83-75cd-4453-bc4f-59d21d682e48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we define HD vtdLists for VTDs in the core and the envelope\n",
      "For core, we grow a circle from HD center\n",
      "For envelope, we pick up all tracts that intersect the line from HDcP to core center, ...\n",
      "...then we grow the district from the midpoint on the line from HDcP to the closest point on the core\n",
      "working on HD 533 our 0th out of 425 that will contain the VRA core, time = 0\n",
      "working on HD 5232 our 50th out of 425 that will contain the VRA core, time = 24\n",
      "working on HD 5314 our 100th out of 425 that will contain the VRA core, time = 49\n",
      "working on HD 5386 our 150th out of 425 that will contain the VRA core, time = 74\n",
      "working on HD 5467 our 200th out of 425 that will contain the VRA core, time = 97\n",
      "working on HD 5530 our 250th out of 425 that will contain the VRA core, time = 121\n",
      "working on HD 6288 our 300th out of 425 that will contain the VRA core, time = 149\n",
      "working on HD 6362 our 350th out of 425 that will contain the VRA core, time = 174\n",
      "working on HD 5292 our 400th out of 425 that will contain the VRA core, time = 198\n"
     ]
    }
   ],
   "source": [
    "hdCP= [tractCP[t] for t in range(nHDs)]\n",
    "print(\"Now we define HD vtdLists for VTDs in the core and the envelope\")\n",
    "print(\"For core, we grow a circle from HD center\")\n",
    "print(\"For envelope, we pick up all tracts that intersect the line from HDcP to core center, ...\")\n",
    "print(\"...then we grow the district from the midpoint on the line from HDcP to the closest point on the core\")\n",
    "coreTractPoly = tractGeom[coreVTDs[0]]\n",
    "for v in coreVTDs:\n",
    "    coreTractPoly = coreTractPoly.union(tractGeom[v])\n",
    "startTime = time.time()\n",
    "VRAvtdList = [list() for t in range(nHDs)]\n",
    "for jj,t in enumerate(coreVTDs+envelopeVs):\n",
    "    if jj%50 == 0:\n",
    "        print(\"working on HD\",t,\"our\",str(jj)+\"th out of\",len(coreVTDs+envelopeVs),\n",
    "              \"that will contain the VRA core, time =\",int(time.time()-startTime) )\n",
    "    VRAvtdList[t] = coreVTDs.copy()\n",
    "    bridgeVTDs = list()\n",
    "    circleCP = hdCP[t]\n",
    "    if t in envelopeVs:  #HD center is pushed toward core, define the connecting bridge\n",
    "        nearestCorePoint = nearest_points(coreTractPoly,tractCP[t])[0]\n",
    "        bridgeLine = LineString([tractCP[t], nearestCorePoint ] )\n",
    "        bridgeTube = bridgeLine.buffer(0.002) #this assures a connection from HD center to the core\n",
    "        for c in range(nCounties):\n",
    "            if bridgeTube.intersects(countyGeom[c]):\n",
    "                for v in set(countyTractList[c]).difference(set(coreVTDs)):\n",
    "                    if bridgeTube.intersects(tractGeom[v]):\n",
    "                        bridgeVTDs.append(v)\n",
    "        circleCP = Point(0.5*(hdCP[t].x + nearestCorePoint.x), 0.5*(hdCP[t].y + nearestCorePoint.y) )\n",
    "    bridgePop = np.sum([tractPop[v] for v in bridgeVTDs])\n",
    "    VRAvtdList[t] += bridgeVTDs\n",
    "    availVTDs = list(set( [i for i in range(nHDs)] ).difference(set(coreVTDs+bridgeVTDs)) )\n",
    "\n",
    "    targetPop = aDP - corePop - bridgePop\n",
    "    if targetPop < 0:\n",
    "        raise Exception(\"Error, core + bridge pop =\", corePop + bridgePop,\"> aDP\",r5(aDP),\"for HD\",t)\n",
    "    nonCorePop, R, toler = 0., 0.01 * VRApoly.area**0.5, 0.005*aDP\n",
    "    dR = 2.*VRApoly.area**0.5\n",
    "    nLoops, maxLoops = 0, 14\n",
    "    while abs(targetPop - nonCorePop ) > toler and nLoops < maxLoops:\n",
    "        nLoops +=1\n",
    "        R += dR * np.sign(targetPop - nonCorePop)\n",
    "        dR *= 0.5\n",
    "        nonCorePop, nonCoreVs = 0., list()\n",
    "        coreCircle = circleCP.buffer(R)\n",
    "        for v in availVTDs:\n",
    "            if coreCircle.contains(tractCP[v]):\n",
    "                nonCorePop += tractPop[v]\n",
    "                nonCoreVs.append(v)\n",
    "    if nLoops >= maxLoops:\n",
    "        print(t,\"WARNING, circle bisection ran\",nLoops,\"and nonCorePop vs target =\",nonCorePop,targetPop)\n",
    "    VRAvtdList[t] += nonCoreVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "41a31c1c-0962-4ad3-893e-98078b3ee3f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying all non-whole vtds in Milwaukee county, and the city boundary\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Displaying all non-whole vtds in Milwaukee county, and the city boundary\")\n",
    "for v in countyTractList[40]:\n",
    "    if tractGeom[v].geom_type != dummyPoly.geom_type:\n",
    "        plotPoly(tractGeom[v])\n",
    "plotPoly(cityBoundary,0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "075d484d-8ccb-43a7-8748-84bd0c33fadb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "establishing which UNITS are in the core\n",
      "the final core unit pop is 674155.0 0.91508 of a district vs vtd-based 0.91508\n"
     ]
    }
   ],
   "source": [
    "print(\"establishing which UNITS are in the core\")\n",
    "coreUnitPop, coreUs = 0., list()\n",
    "coreTractPoly = tractGeom[coreVTDs[0]]\n",
    "for v in coreVTDs[1:] :\n",
    "    coreTractPoly = coreTractPoly.union(tractGeom[v])\n",
    "for u in range(nUnits):\n",
    "    if coreTractPoly.contains(unitCP[u]):\n",
    "        coreUnitPop += unitPop[u]\n",
    "        coreUs.append(u)\n",
    "print(\"the final core unit pop is\",coreUnitPop,r5(coreUnitPop/aDP),\"of a district vs vtd-based\", r5(corePop/aDP))\n",
    "coreUset = set(coreUs)\n",
    "coreVset = set(coreVTDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d69ed2e2-2946-4441-bcb6-61a6014eaa3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "c3ba3e41-3dde-47ec-aec5-18a2e05e3b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now back to polygonal HD drawing prep ... optional squish\n",
      "for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state WI\n",
      "clipPoly 0 intersects [1, 3, 25] counties and a total of 8 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state WI\n",
      "clipPoly 1 intersects [14, 30, 37, 42] counties and a total of 76 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Now back to polygonal HD drawing prep ... optional squish\")\n",
    "print(\"for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "f6674cc7-3ea6-475d-9a4f-f352e29c24c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "11359c48-160e-4979-a539-fff83e286396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "e2d900e5-f4bc-44c5-9cba-91941a868a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 500 is inside the map's convex hull\n",
      "working on confirming HD center 1017 is inside the map's convex hull\n",
      "working on confirming HD center 1517 is inside the map's convex hull\n",
      "working on confirming HD center 2017 is inside the map's convex hull\n",
      "working on confirming HD center 2517 is inside the map's convex hull\n",
      "working on confirming HD center 3017 is inside the map's convex hull\n",
      "working on confirming HD center 3518 is inside the map's convex hull\n",
      "working on confirming HD center 4019 is inside the map's convex hull\n",
      "working on confirming HD center 4519 is inside the map's convex hull\n",
      "working on confirming HD center 5019 is inside the map's convex hull\n",
      "working on confirming HD center 5768 is inside the map's convex hull\n",
      "working on confirming HD center 6346 is inside the map's convex hull\n",
      "working on confirming HD center 6891 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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WdiHRWhUvD+5uO2/cUEXb0lKAHgLljUoD/y6ppXzysBN7DS0OyvY0UphloGJ/Ey6HSGi8NxEpCpzWrQyffyXNrR0sXbyCyuwteLSUIyLQERBLdOY4Fpw1m6AAnxO65qmwsbCBtAgfvLVSMUAJCYnfltOag/JbIAmUk8PU0Ul2bj4F+/OpLynBbqhE12FAIboFhUntA/4R+ETGEJecxIjhqYSHuqckHDYnTdUd7pyRSnfOSFN1B06HCwCfYB0BkR5uQRLpSUCUB1oPVb/G1el0UV6ygxRXC5aAFM4utVJptvHq4Gim9rL6xux08WRJDW9XNfJEYjjXRvRMsP7HwUo2thjZMjqlhyBy2ZwISlmP/X/bV0aF2cbPmYP6Ne7esJodlO5uoCjLQOX+ZlyijYDwXFInXkbc8CB0XioqqupZtmQ5Nbu24dlagQsZnYEJxI0ez/wFM7q53Q40DUYrJQ0mRsednuiNhITEnxtJoEh0sXbjTratWIG1qgh9Z2OXDXyHLgB5YAT+Ue7k1RHDB3c9GG1mB41VRhoqTIeiIkaaazsRXSKCTMAvVHdIhBwSIxEeqLQnPhN4oMNMudmGViZDX7qWjIyzebSomg+qG/lhRCJpnj2naI7kkaJq3q5qYPXIJJL12q792W0d3HOwkv0dFpZlDGJ4L1M9R1NrtTF6az73xYVyY5RblP3ytj9Zd1VLh52tP6+jobCIxopkEEXCk3xJyAgibnggWg8VJWXV/LxkBfW7t+HVXo1DkGMOTmTQmInMnz+9K6F4oPhpT600tSMhIXHakASKBDm5+fzwxqt4NZfSqfRAiEgmJH4QSUOSGZ6WgsehB7rZZKOx4shlvUbaDO5cE7lChn+43j1FE+WeovEP16M4wcTKNruDnxrbyG3vpM3hpMPhJEar5pJQP4YcEiG7c34kbMhcMrfu57boYO6MOf7SYJvLxYTtB4jQqHhvSAw2UeTOA5WsbGonWKVgjI8HjyeEE6Q+9lSGwyVyRV4JeSYz76TG8HV9C+ubjdRY7SgEiNGqmRXgzV/DAwhSK3GJIu0OJx5yOQpZ3+Jl35al6L39iUkdjdlko2RXA4VZBmoKWkAQiEz2JSEziNhhgWj0SgpKKlixeAUNe7bjZarFISiwhCaRPHYi8+dPw0N/fKF1LHaUNhPjryPISyoGKCEhcXqQBMqfnK+/XUbRl29iVXuQctYizj9nNoJMoKHZQmVZG+bqTtprOmisMGJqcRvgKdVy9xTNoahIYJQnPiE65PKTq+wL7gf/KxUGni+vx+JyEaZW4qOQYxdFijqtaOQy7ooJ4frIQPbu+pH9oRO480AleeOHEKDqHpFxOV0IMqFHNOOD6kbuLagCQCMT8FTIeTIxggWB3sj6Eflotju480AFKxrbGeyhZb/JTIhaydwAbxL0GhwukTxTJz83tmF3iSToNBzssGATRQRgkF7DBB8PPBVyIjQqRnrrGaRT4zI1kLtnFxnjZ/e4Zme7jeIcA0XZBmqKWpHJBCIH+5GYEUTMsEDUWgX7D5ay8qfltOzdiWdHPXZBiS08hcHjJzJv7lR02hMTGe0WO7sqWpk8qP9+QxISEhIDjSRQ/qS4XC5efPZ1nDuXYgxJ5vZ/PUK7TcaX3xfgym9D3+nOF+lUCdT5ymn2V6IN1TE2JZBLkkNQKk5ejByNzeXimr2lrG4yMifAixsjgxjpc3i6otZq49UKA29XNTI/0JsbrXv43iuTNc3tbBqd0q2vqoMt/PDcLgCu/s8EdF7dc1v2mcwc7LBQbrZyUYgf4Zpj5764RJFVTe1sajHxZV0zADIBWu1O7o8L5e+RgaiOKgzYanfwXHk9JZ1WJvp6EKJW0e5wkt3ewfbWDqwuF7VWOy4gXqvmltZdTBu7kCBv/THH0tFqpSjHQFGWgbqSNuQKGVGpfiRkBhEzNACVRsHuvQWsXbaS1n078TQ3YpOpcEQMZujEKcyeNRGtpu/VTL+wNK+WuUNCpGKAEhISpxVJoPwJMVus/Pefj6KvyoPhs7j97pv4fnMl5V+XIgpgT/UmKsmXgEhPLB5y2p0umu0OtrSYWNdiJFGn5oXkKEYc54HaHwo7LDxeXM2aJiNvD4llTmDfSZ/LG9u4dm8ZdznyKI+YxAGThWVHJalu/a6YnOVu87+51w8lLv3UogCrm9q5bE8JANdHBuKnVPDvklpeSYni/BC/k+63w+lka2sHX9Q207l9MbaMubyVGoOPsn/5OcZmC8U5BgqzDBjK2pErZcQM8SchM5joIf4o1XJycvNZ+/NKTPlZeFiascrUuKKGMGzyFGbPmIBK1XM6K6+qDW+tkij/U5sikpCQkDhVJIHyJ8NitfH0PQ+gqy8g8rzruOSiBXzx7UGaVtbQHK/jur8NI8Rb2+f5ecZO7jlYxR5jJzdGBXFNeAAOUSRQpUR71BRPaaeV92sa2dhspMJiQy4IxOvUTPP1JFGvQSuXIQBX5JXySHwY10cd35jshbJ6tmd/j0fybHa0drBr3OBu3/RNLVY2f1OIb7COkQtiTzkKYHQ4Sd6Ux72xoe7Izrb9jPTW82ZqzCn1+wv7t6+g3ClwpzOEJL2Gr9ITUB4jV6U32hvNFGW7p4EaKowoVDJi0gJIzAgmKtUPmUJg5679bPx5JZ0Hs9FbW7HItRAzlBFTpjJj2liUCgUWu5ONhY3MHBw8IPcmISEhcSpIAuVPxgvPvIZtx1ISLruFeXOm89Ibu9Dua6d6pDf/uno4Ctnxp24cLpGXK+p5pqwe+y+rV4BMLz2Xh/lzfrAvL1bU82xZHV4KOXMCvEnQaaiz2thtNLOrvQO1TOD/kqLIae/gR0Mr2WNT+/Vgtjhd3PDDO5RFTCS/w8LqkUmkevQtqPrLN3XNfFXXwt2xIWQeFRm6OLcYjVzg7xFBnJdbxNKMREZ4nXr0KHflJ4QmjSI4KpHtrSbOyy3iwbgwbuiHUOuLVkOnW6xkGdhTsQu5QoFvqB7/cD0+gToQRCqr6ikvKsLeVIvKacEhUyL4hhLmo+Xm++9GfZxEYQkJCYnfAkmg/InYtecgK5+8B4ZM5s4H7uD//rcDz/JOFAsiuG5O4gl/cy/osFDcaUEnl1NhsfJzQzurm9u7jt8RHcy1EQHsMppxiSKD9BpitGqa7Q4eKqzmm/oWABaF+vFcclS3vveZzHxd18xFIX6kHCVAHl/+IRv9RtNkdzvAvp0a069IiaHTgLfaG7W8Zx5G3IY9dDrdeTd1U9O7Hbt5fzlf17fgr1TQ6XRSPCmte1Jtcyl8ehG0VsBtu8Hz+KuKSnZvQuMTRFj04Smqew5WsrShjd3jUo+54qc/NDa1sH7dLqI0sRRmGWip7UClVRA3LICEzGAikn0RZLBhSzbbVq3BUZiN1tFJp9IDZXw6o2dMZ/L4DGT9EKwSEhISvwantRaPxG+H0+Hk+5eeQ6b25q47b+Ctr/PxLu0k9PJ4zp8Q3es5v3iZ9MUgvYZB+l9WiHjyl7AAdrSa+GdBFbMDfUjz1JLfYWGKr2e3B66fUsHLKVF4KeS8V92ItpeH4C37y9nfYeG1yoYegsFHIafR7uCh+DBu2F/OW1UN/C3y2FGHxcWLeWDTAwCsu2gd/tru5mPzA735qq6Fc4N8epz7SyJtoErBZD/fnit+Di6DxgL39o43YfrDXYfafviBmnvvQxESQsKa1RQatrCnah2RRQ5GX/Jot24WhfjxUU0T2e0djPY5NU+TdRt2cM45M1HIZYycH0tTtalrGujAtjrUOgVxwwNJzIhnwj/TKc7aTqVFzs61a3EU5ZBzYBOb3vBENWgE42ZOZ/zodEmsSEhI/K6RBMoZyltvfYxXexVD/3Y/7a1ObOvr6czw5eZexIkoiqz+Ipf6qg68vXLRmr5nnMYPzQ1fHDNS0WJ30Opwck9cKEM9tYSq+14hIwgCjyWEUWWxMa+XxNgUDy37Oyy9nutwgUIQODfYl1xjJw8X1bDPZOHB+FACe0n8BMhvzu/aru+s7yFQXkqJ5vnkKOS93N/9caH8Izak12MApJ4D+YvBaoRxtwJgr+ug5fsiTD+7q2876ur48cf30Xo9hawsiIJgOcqceYwYMaqrmyGe7kjRzfkVbkM6uYxhXjouCPYlw0vX9doXdlj4qaGV/R0WTA4nQSolY3z0zA3wxlupwGjsQKVWoTgiJ8g/3AP/cA9GnRVLY9UhsZJVT/7mWuTyChJHDSd5TBTTHh2D0+Vk9dptZK9bi+PADnbsW89atQ/apBFMnDWTURmpkliRkJD43SFN8ZyBHCwq57sHb8cZP4IHHn+QN5/eQX2zmcsfGkWcd8/VGs+v3EH9yi/w6hTxEqehai/FKuYysr6A0e8/D+EZXW1FUSSrvZMWuwNfpYIML12/PEWOh1MUKTfbiNGqevR3z5L3+Eg/nLE+enLbOzG7ur/l7okJ4a7Y7tMsLZYW3tv3HkMDhjIzeuYpj+94NH9dQGdWPdbmAqr3v09NWBjFadGkj1uCrcSHopgw4tou5PzzL+523j8OVmJ0OAlUKWm2O9jWZqLKYmeWvxcPJ4TxXFk939S3oJfLSPfU4amQUWOxk2cy462Qc29cKD5ZOcydMxWXQk6esZM6mx2VIJDioSVac7i+kSiKlO0po2BbEfXleozNFrReKhKGB5KQGUxovDd2h4MVqzeTu34dsoq9qJ0WTBo/PJIzmDJ3Jhnpg3/111JCQuLPiZSD8gfH5XLx2M13oGiv58aX3iBrRxNF35Wxf2Ewr8xNBdwPqvUtRixV1dS+/T5bA2NJ2rsEALlmDErtOEbv+BcHIgMYPKmK1L+uQ+6lZnOrCbPTSbqHjhBt39ESR6uVpo/242y3EnzbCOT9rLvTGyWdVh5Y9gHr/EYx2deTqX6ehKiVlJttPFVa29Vu25gUYrTH9/z4tbAUtND47l4EtRzD2RrKKsqZMmUK1fZK1u38nHBjIAsvuu24kQiXKLKkoY0HCqpotDvwUcj5Z3woF4X4oT7i3FqrjWfL6vmkop6zK8rwnjCar+pbuvJqfmGIh5bbo4NZEOSDKIoUbNtM0tgJiKJIfWl71zRQR6sVvbeK+IwgEjODCY7xwmq38/OKDeRtXI+ich8qlw2jNgDv1JFMnTuT9CEnX5dIQkJC4mgkgfIH54MPvqZx6fvEXXYrU8ZN5INHt1EVr+Vft45Cd2gK4PPaJib4eqJ6+UUM737IutF/x9q5DACN51mY1T5YzE+yPMPFe20BZKn+TeN5/lwVmcBVb28nq7yF6yfHc9/c5F7HYNxcTdtit5eIx6QIfObFnvT95JvMvLD6U/4y6SLG+3r2OF5mtvJhdRO3Rgf121PkdLBnzRekTbv4+A0PUW628lK5gRujgojT9S28Hv/8J97w8cdTr+evEYHMCfAiWqvG7HSR097JBzWNrG02ckmIH7c7WwmKikbn1X2KTXSJ1JW0UZhtoDjbQGe7DQ9fNQkZQSRkBhMU7YnZYmXpz+vZv3k9qqp8lKIdoy4I3yEjmblgDoOTTv53LCEhIQGSQPlDU1FVz8f33IgjPImH/vsk7zybTUOVkdn3ZzAi6PBr0WCzU9xppaW8gsTrLsAuU7JtlgdZ3lZu8a5mo86TfRoZUzztZK5+H4C5KTey5rwdjHxyTVc/ZU/P73UcjlYrTR/vx9VuI+jW4acUQQHIzfmR9BFnn1IfJ8PPpT/Tbmvn/MTzkcuOX2OopvZrstZ8S2fzLLZkjqfDU8GLKVHo5XJ2r/2aYVMvGNDxuVwuvv1hJWFTxpOoV/eajyOKIl/WtXD/noNc5jLx+NxjT3e5XCK1ha0UZRso3mXAbLTjFaBxi5WMYAIiPejoNLN06Vryt2xEXXsApeig3SOEwLTRzDprNoPioo55DQkJCYnekFbx/IF575lnUQgyrr37Tgp21GMqaefANH/uD+r+izY5XBjajUxIScTvYhGMZaQAcY6FFMksfFnxdyI9q7gvexT3TNjDS41vAuCnV/GvhalsK2ni/rkpvYzAjcJHTfDNw3/NW/3VyWvI454N9wCwsnwlb81667jnlJd8QOXmW8ClIGlfJY9f7IeHvJoXUqJAdA74GNeu3864cZmE+fa9AkgQBC4O9aM1p4VHtKHMaTH2Gon6BZlMIDzJl/AkXyZenEh1gVus7NtUQ87yCryDtCRkBDF93GQuvGAe7cYOflq6BtPWjXRsXcLiLT/Q7hlGUPoY5i6YTVxM+IDft4SEhER/kATKGcKXXy/Fo2YfIQuvxVfvxbIvt7N6pAfeiR4c6DAjihCvUyMgsO7zrxjznycxaDT4rV4C5ZtBqWfyN9dABVQMCuOVA4ncNy+Jy8ZGMLhuBCl+Kchlcq4YG8O04WH4/YbTKaejQoyPxgeNXIPFaWF4UP/EVnTsVez1rsbWEk2Jwi1Irgv3Z/earwiMS+/zvI4tW2h48SW8F56N76JFfbZbvq+ODQUNXD85nkg/Ha2t7YQF+/fZ/hfqigq4OHM4X1W080ZlwzEFypHI5DIiU/yITPFj0qJBVB1ooSjbwN711WQvK8c3REdCRhBzJk/n0kvOprnVyE9LVmLcvomOjd/z7cZvMXpHEjZiDHMWzCI6IrRf15WQkJAYCKQpnjMAQ0MLb97+dxx+ETz8wv9Y9V4+FfubeGWeD+dE+3NJqD8CUH+oYJ3y7jvx3bIJgJQDh5bj5i+GLy53b5/1AmRc1eu1vqxr5tb8CgB2j0sl+DdwId2d8yPDDk3xfJ1dxd1f7SbGX8fau6f0WAbtaGmh8rq/Ydm7l5ivvkQ7dOhJX7e+ox6r00qUV/+nLMoMJhbvrGLeqAgCFGYKt3zPoDFn4eXft5lb2cWXYN69Gzji93EUdqeL1IeXYzuUBPvtRVEo9Z4MTYrp1k4URXKNZiI0SgJVShw2G2V7dpGQOZo3Kg08WVxL8aS0EzbpOxKnw0Xl/maKsg2U7G7AbnHiH67vmgbyCdZhaGxl6U8rKduxGV1jCQIiJt8oIjLGMv+sOYSFBJz09SUkJP6YSFM8f0DefOYF5C4Hi+66k4q9zRTurGf6VSn8194AQNIhc7VfTNbMt91MS6A/3uedd7iT5AVw6Veg9oTosd36N1rsLN5dy+g4Pw6YDnuV1FrtpyRQXC4HMlnPt1BLXQd6HzUqTc9jP+RWA1DW1Nlrn+Zdu7Ds3QtA3RNPEPvFFyc9vmB9MEbjfnJ2/QU/vwlER/3tuA62MUEe3DI/mcLcjZS3Ghg+/SpEi+uY53gtWIB5927UKceYNpMJpEf6sKOsmcmDAikpr+GCc2f3aPdudSP/LHS/RjvHDsayJ4v4jNEApHposYki5RYrCTpNj3P7i1zhrv0TkxaAw+6kYp9brGQvr2D7j6UERHqQkBHEufMW4H3lhdTWN7F08QpM2VtoXfUFn6z6ApN/LNGZY5m/YBbBQcePAklISEicKJJAOc0sXb4BbWkW3jMuJTI4hE9f305Uqh+OVG/asuqZ6tdThWoHD0L7+MOgOsITRRBg0Kxer/Gvxfv5KrsKgJ2PzEQlExjmqSXdq/cKuCsa2/imvoWbooJI8+y9TUHB41RWvY+39wgyM77q2p+3rooNn7tdWq/53wS0HiqODNHdMi0Ro8XRZ4E7/ZgxeM2bi7WklIgXX+y1zYlQVv4aLS1baGnZQnjYJSiVfVdf/oWclZ/SYmgjKi4Nw4u5OJstaIcF4r+o91VPfn+5HL+/XH7MPgVB4LO/jaHNbMdUX0uxofd6RA02R9d2bUU5sSFhyOTuBF/FIXHlHMCYp0IpJy49kLj0QOw2J+V5TRRlG8j6qYxt35cQFO1JQkYwF513Lp7XLqKyxsCyxSsw5WylefknfLD8MzoD44gdOYH5Z80gwM9n4AYnISHxp0YSKKeRtnYTWR+/gdMnmtuvvpiNXxZhMzuYfGkSqzvdkY4RR4uI5lJ4ayqYW+DiTyBlwXGv43FEJMNHpeC+uJ65BI02B75KOXJB4M4DlTTaHfxgaKVqYgLlFW+h18UTEnJ4JU5D40r3PbTldOuntf5wZMTa4UB71AqgUbF+fH/T+D7HKtPpCH/mGbCbuwuwkyQwcBYGw1JkMg1y+bH7E10uNn72BjkbWnG2biZftpaF0TcjIGDe3QB9CJT+IpcJ+OlVrNt9gHMW9i4mb40OJkClIFWnxqcwj8DRh1+rkk4rMiBC8+tMyylV8kPTPEHYLI4usbL9xxK2fFtESJwXCRnBXHrRhXj8/XLKKmpZtmQ5Hbu20bj0fd5d+iGdwYkkjJ7AggUz8PHuX66MhISERG9IAuU08tqzr6C2m5h/27+oL21n34ZqJl48CC9/LR017ge9Vn6UMZgh3y1OAHI+6CFQdi7+lg0fv0twXCKXP/UcAA/MS2FachCpYd4oj+4PeKeqoWtaoXxyGlP9PfmqroVR3noqK9+jrOxlABQKDwICpgGQNOhRqqo/ISrq2m59jVwQi8ZDSUicNz7BJyEwRBE+XAil62HYIjj39eOe0t5opjCrnoSMILwDu18zJPgsQoLP6rav6d33aHjhBQJuuJ6A66/v2p+78itqd/khc6hxAlZXJ822z2l0XYRyRBARJ343Pehob0ep0yPrI4dEJ5dxbUQgJbt2Ejkso9uxxQ2thGmULG9sp8ZiQyuXkajTMNpH380EbiBQaRQkjgwmcWQwNrOD0j2NFGUb2PJtEZu+LiQ03puEjGCuuGwR+huvorisip8XL6dj93bqf3yHNxa/jyUkiUFjJzJ/3nS8+ojESUhISPSFJFBOE2s37kSRvxHNuIUkJ8Tw+RM7CI33Zuhk97LOIJX7V1NtsRF1pMNq4iyYfC+4HDD5vh79Hti8HoD6ksKufUq5jImJgYA7CfPoPIzs9sNRD7PTxUsp0fzfoEh0chmGhpKuYzrdYTOvgIBpXWLlSDR6JSPnn4Lpl8MKZe4EYHZ/1i+BsvLd/dSVtLHt+xJuer3nmI6m6e23Ea1WGp5/oZtAMe9pJGJfMS0RM/FmMmmRaq6blsCVP7o4b1zYSd/SkRRUNpAYfezqye2NDah1Hig1h/NMPq1tYm2zEYAb95fjKZdhdYnYRBEfhZwbIoOY6OuBxSXipZAxSK9BNUCiRaVVkDQ6hKTRIVg77ZTkusXK5q8K2fRlAWGDfEjICOaaq/6C1vOvHCgsY8VPKzDn7aDm2zd49bt3sYYlkzJuIvPmTsVD3/v0loSEhMSRSALlNNBptrD+nZcRPUK49Yar2PlTGcZmC/NvTOuqRjzKW48MWNnUzl8jAg+fLFfA1Af67HviJVew9dsvGDq1u6GXw+Hg/fffp6qqirlz5zJ69OiuY/fFhuClkDPVz7PLzfUX19qgwNmMH78ZhdwThULfrc/jVU8+KZQaWme9gEfVOhTT+r7PI9H7nJhFvv9119Hw4osEXHUJOO0gV9JSV0N9axnJgWFMyn2MrWnDmDvveqb46km7zEBx3V6C48adctE9h8PRTXj0hkqrpaOlqdu+beWVxFQWcuP0KcwP9MVfpUAURfI7LHxU08R/y2p5qvRwe7VMYKSXnvlBPiwK8UPTS+TsZFDrlKSMCyVlXCgWk52S3AaKsuvZ8HkBGz4vICLJLVb+dt3VaPR/Y+/+YlYvW4Fl7w4qv3qVl755C3v4YIZMmMzcOZPRak5feQMJCYkzG2mZ8WngmadfxLlrFZP/8W/iAqP56uksRi2IJXNeTLd2C3MKqbLY2DAqGb3i+E6ox6K5uZkXj0g6ffTRR497jtNhZ9PnH+FyOJh42dUolIdzH9pXV9C+shxliJ7g20d07d/XuI+71t+Ft9qbj+Z+hEqu6u4kW5cHm1+EIedD0pwe11yyp4abP90FwKZ7pxLhe/ypgaJ2M5/lVjMrNZjR/v3Me9j8Aqx82L39YAMfP/QPmlsrsDfbuOP977GqZDTY7Jgryvjss88ASE1N5cILL+y1u511O1letpxFyYuI94nv87Lbc/YR5OdNbMyxJ4zaDHW01tehUKmxdpporK4mbngGZTk7SRo/CU+/7st8SzutNB8qANlos5Nr7GRds5H1LUZCVEreTI0hw1vfx9VOHbPRRvEut1ipLmhFJghEpPiRkBFEXHoAap2SXXsOsu7nlbTt34mnuQmrTI0zMpW0SZOZPXMimmNU05aQkDjzkZYZ/87ZkbMX565VyNOnk5k+mK+fzsIvVM/w2T29Oi4J8ePBomr+UVDFK4OjT+m6vr6+TJkyhcrKShYs6Dux1uq0IiCgkqsoydlJ1uJvAWiureb8+x/ramfOawTAXtfR7fzvir6j2lRNtamafU37epqkrXgIStZC3pfwaFuP6x+oNXZtVzab+yVQ/llSw3qniVf2mKibmn7c9oBbKP2C04anfwAtrW5/GJlWgRaI0qpp8PNDqVRit9tJOcYy4vs23IfBbOCLg1+Qd2Ven+1cLrFfURjvoBDaDAZ8Q0LRefsQN3wkAFaTCdHpor3BQEdrC6GJSQDE6tTE4o5GxOnUjPLx4G+RQRR3WrjjQCXn5RbxWVo8447hWnsqaD1VDJkUzpBJ4XS0WSnOcYuVNR/ms+5TgajB/iRkBHHTLdej0t7Mzpz9bFi+AlN+NkUf5bD309cgeijDp0xlxtSxqHqx/ZeQkPhzIQmU3xC7w8FPr7yATOPLPbf/ndyVFTRVmbjgvkzkvYTgQ9RKJvt6Uthh6aW3E0MQBKZMmXLMNhXtFSz6aRHttnZen/E6Q2IS0Hn70NnWSsbc7rV05PoKLE2NqOMVwMSu/ecmnMum6k34a/xJ9U/teZG4KW6B0gfXTYpDRCQl1Iux8f3z10j10LK+xXj8hkcy41HwCIaEGaD2YP5t97L9x3cYs/Cv3ZoFBgZy5513AqDV9p07kRaYxqqKVYR7HMcaXi4Hp+PYbQ4RNSStxz6NhwdN1ZXIFQpKc7PxC4/EZumks7WV4LiEHu3jdRq+So/n0t0lXLevjM2jk3/1oox6bzVpUyNImxqBqcVKcY6Bwqx6Vr23H7lCRvQQt1i59c5bkCsFtu3MY9OKVVgKcsh/dye7PtRCTBqZU6cxbcpolArpY0pC4s+INMXzG/LKi29j3vw9I256mOGDUvniiZ2kTY1g3Pk9Hyzg9iO5fn85t0UFc1tM774hA8mKshXctf4uAGZEzeC5qc/hdLgfpvKjHhJFM2Zir3J7q/TlnvoLOTu+YsSoI6ZGrCZQ6d3eLQOAKIpUWGxEaFTIT6HPrJ/eIXP+X4/fsBdcootGcyNBuqBjttu2u5AQvZyYhDh3/osoguLEpjZEl4hlfxM2lZUmUw0efv5YO0x0tLaQPH5yr+fUW+2M2ZbP9ZGB3NvLMvPfgvYmM8XZ7siKodyIQikjemgAiZlBRA3xR64Q2LR1F1tXrcZWuAud3YhZoUcen87o6dOYPD4T+SlOdUpISPx6SFM8v1P2HSjGtGUJruTxTJ0wku+ezcHDV83Is/pe8VJrtdPpdPUIyzudLla8tY+S3AYW3DyM6CHHjzS0tmWzf/89eHgkMST1pV4dYKdGTuWaIdcgIHDT8JuAnsLkF/yuuRrDf/57XIMyANnRD2D1iU0zuMxmHA0NqKJ6t6wXBIFobfdky46ODuRyOZo+ElJFUaTF2oKfxq9rn0rf3cTN4TAiCErk8uO7tsoE2XHFCQByBS6HHdpr4c0pYKqDiz6EwQuPf+4hTFtqaFviXl0VcXM6qgh33k1bQz1FuSuJHzYdQegekQtWK7kgxJfvDC38IzbkuI66vwZe/lqGz4pi+Kwo2hrMFGXXU5Rt4Oc396JQy4lNCyAhI4q7HrgLQYD1m7PYvmYNjqJcdh3czOa3PFEmpDN25gwmjh1+ygnLEhISZzaSQPkNcLlcfPn8s8iVOm6982b2baymtqiNc+4YjlLV9zdCo8NdsM5P2b1Na10nJbluG/wlL+/u19La6qpPMJvLMZvLsdkMaDQ9l80q5UruyLijX/fkd+ml+F16ab/angqiw0Hp+RdgKylBN2YM0e+/d9xzysvLee89d7trrrmGqF6EzYObH+TH4h8J1gWz6sJVPY63te8mK8tdSiAz42u8vQemurPIocCRYZ9bnABsev6EBEpfVDX8h/3b8lj+5j6Co8dx7l2ZyBWHH+LjfTz4qKaJNofzV5/mOR7egVoy5sSQMSeG1vrOLrGybGc9Ko2c2GGBJGTEM/mhkYDI6vXbyFq7FmdhFln5G1n/ujeaQSOYMGsGY0YOlcSKhMQfEEmg/Aa89+4XeLWUk3TV3cidSrZ8W8zgiWGEJ/ke8zzPQ+HsRpuD+CNyRX1D9SSPDaF8bxPzbuiZp9AboaEX0Ni0Dq02CpUq8Jhty8rK+Pjjj5HJZNx9992oVKdvdYVotWKrrASgc9u2fp1jMBi6tisqKsAHvjz4JTOiZ5AelA5ATr3bAbe+s/6IMw9HFYzGfV3bTU0buguUA0tBdLrrH51gJMLldCLTyCB2Cky8C6xGmPHY8U7rhse4MBT+GhT+WpRBOpoMtWTn5rFc409b68OkmXOpK66lYl8ZscPius4LO1R3qc5mP+0C5Uh8gnVkzoslc14szTUdXWLl4PY61DoFsemBJGUkM/2xsTidTlav3Ur2unXY87eyde9aVqp90SdnMGnODDLTB0tiRULiD8KZ8yn1B6WkrBrD6q9wxIxg/pzJ/PTqHlQaOePO6z3v5EjC1Eo85TI2tBgZ7XN4WkQmE5h+5eATGoef3zgmT8o5fkNg//79OA7lntTW1hId3b8VRO0WO59tryA90ofRcQNTQE6m1xP56it05uTgd8UV/TonPT2djo4O9Ho9mZmZ3LD6BjZXb+aD/R90rbB5fPzjLClZwsVJF/faR2jIuVgs1SiVPkRFHuGWW7oBPl/k3h71d5j3nxO6H9HlQi5Xuf1spj98Quf+giAT0KYcen2X3sPSHQ0UE8NPo6eij1cTVzcIXfNGECO7nfeLcZvNdUalnXXDL0zPqLA4Ri5wi5XCrHqKsgwc2FKLRq8kbnggQzKGMPuJCdgcDlas2sTuDeuw5W1k4+5VLNP44zk4k6lzZzI87dRKE0hISJxeJIHyK+JyufjwmWdRyBT8/e47KNxZT3leE/NuGIpae/yXXi4InBXkw4c1TdwWHTxgzqDHIzMzk5qaGvz8/IiI6L/B+/MrC3l3s9stbP09U4j2HxjfDY+JE/GYOPH4DQ+hVCq7rVhK8k1ic/Xmbm0yQzLJDMnssw+5XEtC/D09D6g9cUdaRPA6cXdZp9OJXD6AiZ4FywkhkmJiyCwpoCJjPI7ZrZwdtACbufsS8FKzFYDQU6hg/VshCAL+4R74h3sw+uw4GitNFGXXU5hlYP+mGrSeSuKHB5GROZwFc6ditdn4ecV69m7cgDV3DWtylvODLhCf1JHMmDebIYP79qaRkJA4M5EEyq/I518uwdNwkMgLbsBTo2fxl3tJyAwidtixp1h+QQAG6TR8XdeCXRT5rSZagoKCuPbaa4/f8CjCfQ8vw/VQnzlvrdtH3M4Fgy4gVD8Aq1fChsMNW0B0QciQvtvtfBs2PgeT74GMq7p2D7hAmf8sM7LeI2PIRLwHTznUtzu6lr+x+3LuT2rc7rRlZhsCAgGqM+d3dCwEQSAwypPAKE/GnBOPodxIUZZ7Gmjvhmp03iriRwQxJmMU5yyYSYfZwrKf17F/8wYsWStZvnMpX+uD8Usbzaz5s0hOjDndtyQhIdEPfh+fUL9DauubKP3xIxyhg7nowvmseGcfoigy8aJBJ9TPhhYj43090B/1UGtubiYnJ4fk5OQTinL0hstmw1ZUhDopCeEYD09jUyOV+/OIzxiNWtfTQO2a8TGMjfMn3FeLt/bM+ZYuCAKRnt2nO0SXi+baanxDw5DJTlAwBLsFwMrGNv5RUMUUP0+eTYrsvjJmzZNgbobFt3UTKKLTiUwxgJGwxBkIiTPw6+VQ6KAUSnOziU13Fx3c1GoC4KycQgTc5RRujw5mqv/vZzm/IAgEx3gRHOPFuPMSqC9rpzCrnuJsA3lrq/DwVRM/IoiJmeO44Lw5GE1mli5bw8EtGzBvW8pPW3/kM89QgtJGM2vBbBLjIo9/UQkJidOCJFB+Jd7+33PIRRdX3X0HZXmNFO6sZ8ZVKei8TiwOUmu1M8an57LcpUuXUlRUxKZNm/plW38sqm64kY7N7imQY3mafPXEg7TUuL1P7vpiSY/jgiAwOKznw+63X9B6fJa/8SL71rlX7/R2L/3hzaoGaq12Pqtt5rnkwyuFHDYbQuZ1yDf+H4y9uds5LqcTeS9LvE+Gxtdfx7R+AyGPPIwmuWe+hU9wCMZGA631dfgEh5AzdjBWl4jF5WK3sZNPa5tZtKeEK8P8eTIxAsVA11X6lRFkAiFx3oTEeTPhgkRqi9soyqqnYGc9u1dX4umnISEjiGmjJ3HxhfNpazexZMkqjNs30bH5R37c/D3tXhGEDB/D3AWziYk6Pf4wEhISvSMJlF+B7xevQleRi9/cKwj1D+SzV7YTlerHoNHHrmLbG2qZDKvL1WN/YGAgRUVFAzFcbFWVPfbt+nkxB7ZsZPLl1xA2yP3wG9CpidNMU1XFKfdxZVgAWW2dTPT16KoS3VBRxqcP3InDbmPR49u7XrtfcLlcA2I25mhspOH5FwAoPefcPoVlSPwgdi7+hjHnX0KY5rA4TvHQcnGIHx/XNnF/QRVyQeDfg04tEnc6EWQCYYk+hCX6MOHiQdQUtlKUVU/+1lp2razAK1BLQkYQcybPwP/Sc2hqaeOnJasw7tiMcf23fL3+a4w+UYSNGMu8s2YTGdYPTxsJCYlfFUmgDDDNrUbyvngHh38cd1xxARs/L8TS6WDypUknZY6VoFOzx2jusX/WrFlkZGTg63vspcr9IeLFlzCuWonPOecA4LDbWfP+myCKfPbQ3V0RhgsefIKag/nEpI84Rm8DT2tdLYayYuIzRyNXDMzU0Zwb7mDfhtWkTJhy3LY/GlrZ1d7BTVHB3fI2FgT5sCDIp1vbuqICHHYbAAe2rO9FoDiRyw/3ITpdtK+qAAG8pkch9LPqsNzPD/3EiXRs3EjQPb0k8x5CqdEQnzmG5uoqAiK7r8YSBIG/hAXgFOG+girmBXozwbefxRbPYGQygYgkXyKSfJl0ySCqD7ZSmF3Pvg3V5Pxcjk+wjoSMIBbMmIP/FRdgaGhhyeIVGLO20L7mSz5f8yUmvxgiM8Yy/6zZhAYPzIo0CQmJE0MSKAPM6/97EaXDwoV33EldcTt7N1Qz8eJBePn3XcflWMwO8Obr+hZ2GzsZ5nk470MQBAICAo5xZv/RJA1i90EFWY8fJHWSicmLBjFozAQKtm5k5Nnnd7XT+/iSOHrcgFyzvzgddj558C4sxnYQBO76fPEJnS+6RCoPNOMdqMU78PDr5x8RyaRLr+p5gtUEHyyAml1w4fs0DzqLv+8rQwReq2w4bjHCZFc2jX7VqDz9GHtFz0Rjl9OJ4ogcFPP+Zoxr3REsR4MZ/8v6Lkh4JIJMRtRbb3b9XN9RT3Z9NpMjJ6NXdl89pVAqMZSV0N5owDswGN+w8G55N1eG+fN5bTPPl9X3KlDaHU6qLTbkgkC0VoX6d+QzIpPLiBzsR+RgPyYvSqLqQAtFWfXsWVtF1tIyfEP1JGYGce68BfheczE1dY0sXbwCU/YWWlZ+xkcrP6cjIJaYkeOZv2AWQQE+p/uWJCT+NEgCZQBZuWYr6sKt6CadT0J0JJ8/sYOQOG+GTj5OAbljMDfAm0SdmvsOVvHDiIRfbanxvo3V7v83VDPl0iTiL7qBUdfcRrDX8W3ej8/hyFFnXiPNn+Qj81IReu8oBHn/o0rCSWSz7F5Tyeav3VNhix4ZjV9o30ufRYCGg25xAvDd9Xg8sJAEnZrCTivnBR8/WiUU/ERzQDjRQn2vybeiy4XsiPIBylA9gkaOaHGiyzj5ekt/W/k3Strc9vdHV1P2j4jCPyIKURQpKqnEkJ2DRnH4tfTw82dRkDcPlNTSZnfgrVTgEkW+qmvhneqGbhE8pSAw1c+Tm6KCunnzHInF6aLeZkchCISqlchOg61+b/xSqDB6iD9T7C4q8pspyqpn14oKdiwuxT/cg4TMIC5YeA4+111KRVU9y5b8TMeu7TQt+5D3l31MZ2ACcaPHM3/BDPx9vY9/UQkJiZNGEigDhKnDzJYPXsXlFc6tf/8LOxeXYWy2MO+GNIRTSD5UyATOD/bl6dI6ijutpHicXCTmeIw5J55dKyrInBvN4t013PKZ+yH9/U3jSY/0GbDrmHe7XV5d7TZEuxNBfuy3oFyh5LInn3VP8WSMOuHrOR2H83dczp65PEciAISlw5gboS4PznkNlUzGqpFJtNqdBPfDP+SDsId5vMIOwKdFjYxL6B7lEl3ObsJFGaAl7J+j3ddXnnxuikw4vnBdsb+ev3/kFi+r7pxEQpAnoihiamkioqCIqPIKslUdBKoU/KfRwhqlB1MDfXkhOZAEnRqbKJJn7OTz2mYW7iri+shAHo4PQyYIiKLI4oY23q1qIKu9A8chLzhPuYzZAd7cEBVE6q/03j0Z5EoZsWkBxKYF4LA5qdjXTGF2PdnLytj+QwmBUZ4kZASx6IKL8Lr+SkrKqvl5yQo6dm+jYcl7vP3Th5iDExk0ZiLz50/H2+vE6ktJSEgcH0mgDBCvPf8aWksbM+54gNYaM7tWVjBqQewxv7H3B6PDySsVBi4O8fvVxAnA4PFhDB7vNh7bvKm0a7+h3TIAvR92LvWcHImz3YY63gfhGHWIjsQnOASf4BNPMAZInxmFp78G3xA9ARHHya8QRZDJYc5T3XarZTKC1f2LXHmGpwB7AND34gUjulw9rNiPJUzsdjsbNmxAr9czevToPvOY3pr1Fjn1OUwIn9BnX0UGU9d2SUMHCUGeCIKAp18A4UP1lJo1KJPjubu4Gv/i3bw6YTyDtU4wNYEJ1Go1I4KDuTYikDcrG3isuAanKHJPbCjX7ytjTbOR8T4e/CshnHidBpvLxR6jmS/rmvku6yD3xIRwW3TwaSlUeCwUKjlxwwOJGx6I3eqkLK+R4mwDO5aUsvW7YoJivEjMDOIvly7C8+arKSipYMXi5XTu2UHt92/x2g/vYQ1NJnnsBObPn4aHvucSfAkJiRNHEigDwJbtu2HPWlSZc0gfmsRXT2fhF6pn+Ozeq+/2FxH4wdBKp8vFvbEn94A+GS4fE41KISPST8fkQf0zlesvqkhPgm5MH9A+j4VcLmPQyP6+dqduAX9hZgSxgXpCvDRE+vV8UDlM7QgnME2Xm5vLxo0bAbDZbEyaNKnXdgHaAGbFzDpmX1eNi8HhFIkL1DMrtftrUml2J/Z+WttEfoeVZ6NCWDgstVsbi8VCXV0dNpuN6YBD4+Ddvfl8WFqDRqvl47Q4ZhzlqTIzwJtbo4N5tqyOp0vrcIhw92/4Xj5RlGo5iZnBJGYGY7M4KMtrpCjLwNbvi9n8dREhcd4kZAZx9ZVXoPe5jv0HS1n503Ise3dQ9c3rvPztO9jCUxg8YTLz5kxGpx2IKVIJiT8nkkA5RSxWGyveeAF0Adx7y7XkrqqkqcrEBfdlIu/nioy+EIBtrSaGeui6LREFsFhqqKh8F3+/Sfj79/7QOllUChmXj+lf/Z3+cWZ9Y+6bUx+nIAiMjOnNNs2NXHdiq2RCQg4/zGNjY096XOCO6Nw2I7HXY9vbOlAKAsub2rk+MpCQopoebTQaTbe6TAkJIu9tysO7uYn/BOuIbq6nsLkemUxGYGAgnp7uCI1SJnBvXCgKQeB/ZXVM9fMkw/v4kUW7S2RLq4mtrSbqrHb0chlJeg2zArwJ+Q3s+lUaBYNGhjBoZAhWs4Oy3Q0UZhvY8k0Rm74qJCzBh4SMIK679hp0Xteze28Ba5etxLJvJ+Wfv8QLX76BI2IwQydOYfasiWg16l99zBISfyQkgXKKvPHKu+g7Ghhz+2NY25zsWFzKsBlRBEUPjDtnndVOtLanuVtR8X+pr/+Rysr3mDwpF4Xi97889HTzW8w8OB0O8va7k3YFBAQZCIIMQRC6rh8S5I+Pt/v3GRkZyb333otcLv9Vq0qHqpXYRRG7U+SK8ADKjrLYsVkcyBUy5EesQBIEgR9HJlNrtZN5hOBwOp00NDRQX1/fre0Vvr4s0al5obyeD9PiOBYrG9t4uKiaUrONIJWCCI2KDqeL92saeaCwir+EBfBgXCj6AfCU6Q9qrYKkMaEkjQnF0mGndHcDRVkGNn5ZyMYvCghP8iUhI4i/33AdWo+byMnNZ+3PK7HlZ1H8yfM899lruKKHkj55CrOmj0elOnOcliUkzlQkgXIK5O4twLJjGULqJCaMHs53z+ag91Uz6qxT+6Z7JEqZgKUXozYvzyHU1/8IgEz2W1XpOVnO3Oq5R/JbjHLWzAlUVNUjii5EF7hcIi6XHRH3kmiAVas3ccF5c7vO0WoHLvfI1GLFZnbgF9Y9gnFlWACLDa002BxEaFSUHXGsprCV755xV8K+5OFR+IcdTggN16gIPyq6J5fLCfbzIzgoqGs6y+Vy0dLSwgWuDj4sqCBPZUcjl+Ht7Y2/v383E8DXKww8WlzDFF9PXh0cQ7qntitvpdXu4OOaJp4rr2dHm4kvhyXg/xvXFNLolaSMCyNlXBhmk42SXQ0UZRtY/+lB1n9WQGSyL/EZQdx0yw2otHJ27trPxp9X0nkwm4PvZ7H7o1cgZigjpkxlxrSxKBXSx7CERG9IfxknidPh5LsXn0Wm8uSOO25i36YaaovaWHjHcJT9TP7sD8l6Dd8bWnGJYrflmlFRfyUoaB4qVQAy2a/zbazMbOXlcgPT/D2ZF+jzq1yjPxRlbadiby6ZC87DK+DkcmJKSkqoqalh5MiRqNUnFmpvqCijtuAAyRMmo9IcXyw4nWYqK99Hq4shOGhut2Meeh2Dk44tYCsrq09ofP2lo9XKJ49uw2F1MmRyOJMXJXUdU8gEvh+RiN3VU6bVl7V3bVcfbOkmUHqjMyeH8ksvAyB+1UpUERHIZDL8/f2ZOETL45YC7KERDPHS0d7eTmlpKaLovm52ewevVLdwY3I8DyVG9kio9VEquDk6mOn+XlyQW8x1+8r4Oj3+tC1l1nqoSJ0YTurEcDrbbZTsMlCYZWDtxwdY/+lBIlP8SMgM4pY7b0GplrF1+x42r1yJpXAX+9/ZQfYHOuRxwxg5bRpTJ44aEJdhCYk/CpJAOUnefvsTvNqqSL32PgS7jC3fFjF4QhgRSafu7Hok8wJ9eK2ygdVN7cwM6O67oNH8urVD/q+klu8MrXxc28TBCUPwVp7s2+XkHx4Om43Fzz6Fy+lg17LFJ1U3x2w28/HHH+NyuVi1atUJ1S5yOhx88ei9WDs6WPnWy/26flXVhxSX/A8AYcirBAXNPuEx/xpYOx04rE4A6kraem2j7GVJfOqEMDparHj4qRk65fh2+J07dnZtd2zegurii7p+9jgUKTG7XAiCgLe3N97e7ve1zeXi0u0HGBESxOUKR7dSDlqtluDgYJRKtxhP8dDyRmo0F+QW84OhlXP74VHTYLOzvLGdvSYz7Q4ngUoFo3z0TPXzQneK+WIAOi8VQyZHMGRyBB2tVopyDBRnG1j9fj4yxQGiBvuTmBnC7f+4A4VKxvrN2WxfvQZ70S52v76Vre94oIxPZ/SM6Uwen9FjtZeExJ8NSaCcBAUlFbSs+w5n/ChmzxjPT6/uQaWWM+68+AG9jghkeukY463nwcJqJvl5/qYunqN9PPjO0AqAdgA+wE8GuVJJUGwcdUUFxAw7OYt9hUKBh4cH7e3t3ZI8+4MgE1BpdFg7OlBp+7d8VKeLPWI7psdx0eGg9pFH6Nyxk4iXX0KTlNSjza+BX5ieeTcMpdbQwcf1TWz6bBf/vSANzXH8V1RaBRMu6j25tjd8Lr4IW0UFyvAwfC66sNuxaot7tVBgLzkYa5uNVFpsfDgyibijltR3dnZSVVWFw+Ho2hej1TLVS8sH1Y3HFChWl4v/K6njneoG7C6RQXoNPgo52W0dvFHVQKBKwT/jQrkkdOAs7fU+aoZNi2TYtEiMzRaKcwwUZRtY+e5+5Eq3YVxCRhQT7r8bmRzWbtzBzjVrcBTlkHNgExvf9EKTOJzxM2cydnSaJFYk/pRIAuUEcblcfPrMs8gVam64+1YKs+opz2ti3g1DUesGfqpFEARitGryOyzYXSL9tOMYEK4KD+CcIB+8FPLTFkIXBIFF//ovZmM7ep+Ti04plUpuuOEG2tvbCQ4+MbdWmUzOZf9+lsbKciJTh/brnMDAWYwbuw6FwgulsqfbqOXAQdq++RaA0oXnHLOC9EATOyyQ9ZtNLN1bB0CEr5Z75/SshHwqKHx9Cfv3k70e29RqwlshJ17Xc5ptY4uRGK2qh9+PxWRi9/LFhCWmkJiW3rW/s7OTybWNvFtRz169C7VM1hVpkSsUtDucWF0i1+0rY7exk1uigrkmIgC/IyKBxZ0Wni2r5/YDlewzmflXQviA+7R4+mlInxFF+owo2hvNFGW7xcqKt/ehUMmISQsgISOeKQ+NQpSJrF67jex1a7Ef2MG2fetZrfZBmzSCibNmMiojVRIrEn8aJIFygnz8yXd4NhYRc8kt6FU6fvgij4SMIGKHDaxfCMAeQzZbayx8U9nALH8vPiysO7EOamtI6wg8rlvrKXMof6C3ZTAi4CqrorxplbvJyV4CaDzi56P7EfvZdwV5fR6z1LX0ul/v43vC4kirjezzmDoxAf348XRs3kzkm2/0OF5bWc2SpWsRRZEjU0IEwf1Se/l4MnXCyK79TqeZvftuo6VlOxkjPsfT89j1fMYnBOCjU9LaaWdWyVaK599N8P334zFh/And48ngo5BjdDjZ2mrqUfen1monppeluFu+/oRdy9w1mK554U18Q9yGgjqdjpT4eCpM4BkVQ5RGxZKqer5bv4MCYwdWpwgCyFRqvhqXzih/nx59x+s0vDI4mgwvHQ8UVhOn03B1+MDUuOoNrwAtI2ZHM2J2NK2Gzi6x8vMbe1Gq5cQOCyApI4mZj47DKTpZsXozuevXYd+7mc171rBc44dHSiZT5swgI33wrzZOCYkzAUmgnACVNQaqln2GI2Io5587m5Xv7kMURSZePGjAryWKIjanjR9M0Xh6RfByZuoJT7NkF7/H0JlTUGlOr7PlwS80RM8cWK+WX4M9K0p+k+vI1Gqi3nm7z+N/vc6dYCo7tPT46G/0PyxZ0+3ntrYcGhtXA7An7++MH7fhmNdPDPYk9+FZuGw2DqYNwwZUXnvtbxLJ+VtkIMsb23iwsJrVI5OQH7q3GouNDc1GRnj19Ef5RZAAqHXdj6sO5cyM3pZPvFZNsdlKpm8glyQmEK1VYxdFBslB395CYXND13lH57RcExFIfoeFp0pqOCfIB9+TzrfqPz5BOjLnxpA5N4aWuo4usVKwox6VVkHcsACGZAxh7hOTsDsd/LxiA3kb12PbvY51u1awWBuAd+pIps2dybAhA/8ZJCFxupEEygnw7v+eRQH89e47KMtrpGBHPdOvSkHnNbDLfF2iyD0HK/myop6ZkYO4Jzakhzipyt/Lxk8/IGHUWDIXnNtrWFoUXdCLqBFFkZvzK/imvoWXU6K4IKSnsZgoilg67Gj0yj5D3qLoorDoKVpbtjM49Rk89P3PU2itr3MnSQadfIG8vrA4XTxdWotSEPhHbGiviZ+nwp49e6ipqWHixIno9adWyqClw8ZLa4pIDvXkokx31EVxHCEqHrUg2tt7BP7+k2lp2crQoa/2+9oylQrv886j7dtvCbzt1j7b2Z0u/rfiIDaHi3vnJB83Z+VYyAWB++JCOWdXEeO25TPKR0+qXsuLFfXo5XJujArqcc7wOWcRNTQdTz//HnlAI730vD8kljKzlb0mM48khDEroJcifgHdI2C/5LRYrVY0Gg1RUVH8IzaEz2qbeKuqgbtiQrrE02+Bb4iekfNjGTk/lqYaE0VZbrFyYFsdap2CuOGBjMgYztnzpmGx2Vj683r2b16PNXsVq7KW8Z0+CN/UkcxcMOe4q8QkJH4vSAKln3z97TI8qvcSfPZfCfTx47MXtxM12I+k0QNn27291cQntU1saTVRY7FzU2Qg/xza+4fN1m8+p6Ygn5qCfEaedV7vHbpc0Mt8tcHm4Jt693TGzfkVPQSKy2xmxUPfUmwKRa6Ucf1LU3rtvqOjiMrKd91j3z6H6dOK+3WfdcWFfPLAHQDMvv42hkyd2e14c00HBTvqGDQqpIdfR39Y1tjG65Xub8s1VjuvDB44V1yTycS337rzR7Zt23aCK4Jc/PxGHmV5TZx9azqRg/14e1MJ72521z4K8dIwqR+lBY5+bMrlWtKHvdvvcRxJ2L+f7DVf5MhrrD/YwBvr3dGlg3VGPr1uTL/67txtwF7fieekCGSawx81o7313BYdTJXFRmGHla/rWhjmqeOzYXF9Ri78w3ufMlPIBOYEnlhV4V2GXWyo2sDFSRcTq4/FYrFQVFSEKIpc7KPl2bJ6VjS282JKFINPQ4FD/zAP/M/2YNRZsTRVmyjMMlCUVU/+5lo0HkrihwcyJmMk5y2cRafZzNKla8nfshHzzuUs2/ETX3iEEJg2mllnzWZQ3KmV25CQOJ1IAqUfGBpbyf/mfZyBidyxaCEbvyjE0ulg8mVJA5JQ12Z3cE9BFT8aWonTqpkf4MPsAG/Ezo4+z0keN4mKvFx8gkMRRbGPCIrYa5XbIJWCv0UE8r2hhZdTej6825cupb7WAZ7gtPddAVini8HXdywtLVtJHfxcP+8WOlqbu7Ybykt7HF/1/n4aKoxk/1zOTa9P63e/v5DmqcVTLsPodLEotG/b+ZNBo9EQEBBAY2Mjw4cPP6Fzm2s6KMtrAuDHF3O56fVpDIvw6TqeGHzmVMQ9MkaTEuZFgIeaRpOVayf279u5o9VC82cHATCuqSTi6YldxwRB4P64w0vkDVY7Xgo5mt9opdgta26hzdrG23lvk3dlHhqNhkGDBiGKIrdXVTHFZeHNxkbOzrbweXpCN5fc3xJBEAiI8CQgwpMxC+NoqDB2RVb2baxB66UiYXggEzLHceH582g3dfDTT6sxbttIx9YlLN7yA+1e4QQPG8OcBbOIiwk/LfchIXGySAKlH7z1zAsoXDYuufNO6orb2bu+mokXJ+Llf+rfrkwOJxfvLqHUbOXVwdGcE+TTtWJmSUvvSZsAQ6fNYui0YxeHA7edeo99gsC/EsP5V2LvH1iaoUNJfvZuqv1HknHjnD77lslUjBj+8XHHcDRxI0Yx+/rbkMnlpEyc2uO4b4iOhgrjCff7C/E6DXsnDEEAVAO84kGhUHD99ddjtVpPeHrHP1xP0ugQKvObmX9TGgCzUkPIfXgmOpUClaL7WEVRxOFyoJQfvTrst11RFe6jZev90xBFeozxpfJ6trd18GRiONHawwmuMq0SubcKZ5sN/XGijEG/QV2dI4n3jifHkEOwrvv0oiAIREZGEhkZyejWNu7aksMDa7fw4YxxhHicHpFy5NiCor0IivZi7Hnx1Je1d4mVvPXV6L1VxGcEMXXUFBZdfDYt7SZ+WrIS4/ZNmDZ+x7cbv8HoHUnYiDHMXTCHqIiBn1qVkBhoBPEXC8czhPb2dry9vWlra8PLa2Dq2ZwKy5ZvYP+7/8Fz2iVcc80ivnhiJxq9knPvHoFsAHIb7j5QyXeGFr4fnsBQTx1Wp5XttdsREAjQBpDi716RYa+rw5yTg8eUKch0/Ut63fH9G2ScfS0m4x5277kOQVAwbuwa5PLjn++y2dz1YZSn/vA4+MUGki7uf5KsyyXSWteJT4huQF7j/rJkRQkLZh27RsxvhcPl4MplV7KncQ/XD7uem9Jv6jr2w5LVLFww/Ve9/qbly5gwe+4x21RabIzcur/r57qp6d2Ou2xORLMDufeJOfeKokjBjnpEUSRpdMhJRSktxa00vuVesRX2yFhk2sPfxWxOG9WmamK8Yo7Zd63VxuRt+VzaXsf8ED9iYmIIDHRPwdldIj8aWlje1E5JpxWbSyRco2SSrycXhfj9Zvb7okukrqSNwmy3KVxnuw0PXzUJGUEkZAQTFONJQ1MbPy1ZQfnOLegaSxAQMflGE5ExhoXnLiAowOc3GavEH5+Bfn6f0l/R008/zf33389tt93G888/37V/69at/POf/2T79u3I5XLS09NZvnz5gNYU+S1oN3ay8+M3cPpEcftfF7FjSRntTWbmXj/0hB6c9VY71RYbCplAvE6N/pCbZnGnhY9rm/h3YjhDPd2iYVPVJqZETkEu656IWH7lldjLKwD6v9pCdH/zqqtfjN3ujsa0te3Cz+/4y0llv2JhOoDW1iwEQY63d89pEplM6JF7UmWs4u71d2N32flgzgd4qAZ+OsTZ6Th+o9+IFksLexr3APD67te7CZSTiaDkNeTx5p43mR83nzmxfUfFjkVj41ra2ncRFXk1SqUvISolmV46sto7eSjevdJGFEUcogOlTIlMJYeTKPtQld/Cqvfcwqe2qI2pl/ft0/Lvpfm8uaGERaOieOq8wz41lv1NXdu2KiOaxMNJsiq5iljv409VhapVLAoLYKuhmr9Hum33Dxw4QJlD5DGjk1KLg3RPHSO8dCgFgVKzladLa3mmrI6H4sO4Isy/hwBqsztY0dROVlsHTXYHPgoFw7y0zAnw7tW8DsDhEtnYYmRji4laqw2NXEaCTsOcAC/idRpCE3wITfBhwoWJ1Ba1UpRl4OD2OnJXVeIVoCEhI4gF0+YScOWF1BmaWbp4BabsLbSu+oJ31nyLKm0Sl//1LwQHDZxRnYTEQHDSAmXnzp288cYbpKWlddu/detW5syZw/33389LL72EQqFg9+7dv0tzodeefRm13cScWx6huaaTXSsqGLUgBr/Q44d7HS6R92saeb+6kaJOa9d+GTDFz5MAlYLFhlaCVAouPeRgaXfZUcqVPcRJfygoKOCbb74hPDycyy+/vOv1FhAID7uEttYs1Opg5PIU3n//fSwWC1deeeUJicaWDhs3fpLD/tp2ltwygUi/npGYTZ9/RM7SH5h2zfUMmTLj8IGd78DyB3ANv4b3jQeJDtoNQELCfURHXeduI4qw821oq4JJ94D6sAhZVrqMfU37APi28FuuSL2iX2Nua9tFQ+MqwsMWodUe26Zdpj251SnGDVWY85vxOSsO1XHq1PSXQF0g9468l12GXdydeXe3YyeT9vRCzgtsr9vOuqp1/RIoR1/C4TCyJ+96RNFBWdkrTJ9WjFImsHhEIi7cq3NsThuXL72c/OZ8bhtxG9cOvbbXvkVRZOPGjbS0tDBr1qwe70GtlxKZTMDlEvEPP/br+cXOSgA+21HRTaB4jAvD3mBGGahFHe9zzD462qxs/qoQT38NY86J7yYqpvp7strppNbmZERICHtlKm7KKWCszcgTEYFMG5rY1V4URZpsDv6vrI57C6qosdq7cm1cosgblQ08W1aH0ekiWa8hSKWg3Gzjs7omHiqs5u+RQdwdE9Jt1dn6ZiMPFFRRbLYSqlYSrVFhcYl8X9/C48U1LAzy4cnECAJUCmQygfBBvoQP8mXixYlUF7rFSt6mGnKWVyD4qdCl+pI5ZTZ/vXYRZRW1fPHeRzh2r+Pd29ajHDKJy669gtBgSahInBmclEAxmUxcdtllvPXWWzzxxBPdjt1xxx3ceuut3HfffV37kn4jK++BZP3mbGT7N6IeexZDUxL46uks/EL1DJ91/BUh9VY7V+SVkGc0szDIh3tjQ4nTqbG6XOwxmvmwooR99Qe4IMCXKf6+yAUnIKOwpZBYr1jWFzRw/zd7GBPnzzMXDUMQBKI//LBriqc3srKysFqtlJSU4HK5DgsUQcDDYxCjRrkrH2/bto2ysjIANm7cyKxZ3fNY9jbu5efSnzl/0Pk9vmVuKGxga4n7m+lji/fx9pUjux13Ohxs//5LEEWWv/Z8d4Gy9WVwWKh94TNGaNU03ebebeiopesVrd8HSw89jHd9BP847EsyI3oGPxb/SLutnbPjzz7u7+AX9uTdiM1moLz89eOuMjqZqQRXp522pe5EX8OLu7olgx4Ls9nMCx+8wA/yH7Br7Px8wc8EaLsbhF0++HIuH3z5CY+pNyZHTmZ73XZ0iv574rhcLhobG/H390cm06DRRGA2l+HvP7mrjSAI/CLrDJ0G8pvd0b0Xcl7oU6DU1dWxZo3byyUvL48HH3yw2/GACE8ufWwMIOIdeOzx/nNeCu9uLuW26d2XuCv8tQReM6Rf97lvQzWFWQYAfEP1JI85nMAbqlYhINLqcFBrtfG3fWWMCfTj3aEjEO12CgsLsVqtOJ1Ompqa8PDw4GpPT0JVNj7L2UMyds6KieCW/Aq+M7RyTXgAt0QHEap2RyhFUaTF4eStygZerjBw0NDOS6Eh6AJ0fNLaxr0FVYz38eDlwdHdqjqbnS6+q2/hyZJa5mQf5Jv0hK4coJJOK8sa28h3mSlIEtgb6klMvZ2hlXYStxnYtLGeJQFKMmZH84+H7qGqroHP3v4AR956PrhtA0GzLuKqqy7+XX6plPhjcVIC5aabbmL+/PnMmDGjm0AxGAxs376dyy67jHHjxlFcXExycjJPPvkkEyZM6LUvq9WK1Xo4wtDe3t5ru98Ss8XK2rdeAn0Qt914NbmrKmmqMnHBfZnIFcf+ozU5nFyQW4TR4WJJRmIP46l0Ty0htixmj/kLgiDQYe9gZ+1OnKITnVLHYP/BPPTNDmraLHy7q5r/XTgMQQBlcDDKuX3nBIwePZq6ujqioqK6PliO9ssAt1jcuXMnzc3NjB49usfxe9bfQ5Wpig/2f0Deld1dVyclBjI61o99Ne08vCC1x7lyhYKRZ51H1uLvmP7X67sd+yR5Is/W2FnQ5OSSleD/goLHFsl5btf38MUzsOgLiBwFnqFgrMUa9hdkjWaUAe5v17HesSw+d3Gf998Xen0CNpsBpbIfTrAnkY0laBVokv2wHGjGa0b/l3TW1NSwp3MPJn8TuODLg19yY/qN/Tr3ZNLG/jL4L1ySfAnKE6h8vXz5crZu34oSJw89+AijRy3BZmvs0yU3wjOCuzLuYl/TPu4ZeU+f/fr6+uLn50dzczOzZ/deSNE7sH+RvYtGRnLRyL5de/tDRIofO38qc28ndV/1ZXI4EUTQKuQ8V1aPQhB4dXCUO/larWbQoEE4nU4EQUAURYqLi4mOjuZ2pZLsvDI+3rWX/MpqTAW1fJIxmumDDiem26pN2GtNaDUKbpWpmOkRwLMltTytVnFBZSv/tDZzVWQgTySG9/Bk0cplXBrmzyQ/Ty7ILeLKvFI+SYvjX8U1/GBoRSeXMVivIVqr5o6YYMb7euKlkNNstrFqZw2Na6sp/qSIF5aVEzMtgtsfuJOGhit574VXaVn+CU8VF3HXw/eiUf+6U70SEsfihJNkP//8c5588kl27tyJRqNhypQppKen8/zzz7Nt2zbGjh2Ln58f//vf/0hPT+fDDz/k1VdfZe/evSQm9jTyevTRR3nsscd67D+dSbLP/edl7NkrmHT3kyRHxfP5EzsYOiWC8ecnHPfc+wuq+KKumRWZg0jQaXocX1W+inFh49Ap+/5muGJfHXd8kUtGjB/vXzXypBNFd3z3BqPO/fsJnXP72ttZXbEatVxN1uVZJ3Xdozn4xQZukT9BbUctAF885aAkCsJcNkZMOGRgr/KAB6rBbqbpk1zMB9z5IP2NSPSFy2XHYqlCqz12QiScWpJsX0u9+8LhcPDZks/4pPUTdD463p/3Pl6q/r3ff/xpDWfPP/Hl1yfC5uXL2Fi/m0/ETwBYWllD5OU/Qow7f8lkOkhrWzYhwWejUJz4tJbL5cLlcqFQnBkLCV0usVfX3tcrDHy5eSsfzhjP1PwarosM5B+x/asivrOtg7NyCgF4tKWdK+KSUSf6Itcr3UaI+c1oB/sj2l0ISveXio9qGrnnYBUqp8iNbTJuigxC4alCUMlRhXt0+cmIdieiS0SmVpBvMjN950FUMgGdXMaD8WGcF+R7zGXbTlHk5S2lVK6tIbbKhkkvI3FhDOdNiuHttz+leeXndPhFc9Pjj0tJtBL9ZqCTZE9IoFRWVpKZmcnKlSu7ck+OFChbtmxh/Pjx3H///fz73//uOi8tLY358+fz1FNP9eiztwhKZGTkaRMo2bn7Wf3UfciGTeOu+27j++d2YWq1cslDo1AelfBncjgpt9iwOl2EapQoBRkjtuzjntgQrgnzYFvtNlQyFSIigdpAZIKMdls7I0NG9nH1k6SjCRoOQNTYbsZsRbvWUbtrM6nzLscvpH9mZU6XkxpTDRGeEQNWNC3/07XsE/bxP9fHnN8YzV9Mwwi46UbkGhXCygegZhdc+B4OwmhfWY6j1YKt1B1JO1WBciKsWFOGzdG370t/8OxwoPXuHqWQW5041Sfvvno0LZVlJAed2rJXh8NBua0BlYeqK9/EYrOhUalAgLaWVopjG3kz/00AHmhsZtGgC+Gs53G57GzcNAqHw/076mvqrMnm4IOaRsb6eDDWZwByc0QRCleCRyCEdU+u7rQ5ePiHfbSZ7Tx70TA8NUf8DiztoPY8ZvLOypUrUau7rzZyukTeqW4gxGHlkjmzWbC/isUjEhl5hC+KKLooKnqadmMeg1P+2y3PySWKhK1z51qt1alQd7gIC/8lmRgUAdquCGHXNUWR8EPnfJuewFgfPYIg4DTasNd1IDpF98lOEUEjR7Q6UUV48kCdgS2tJr4YFk+Ypv9RD1EU2XSwkbXfFeFfbsY1yp+brkpj+apN7HrveewqPeff+whDBg9spXaJPyandRVPdnY2BoOBESMOl713Op1s2LCBl19+mYMH3cZMgwd3L2KVkpJCRUVFr32q1eoeHwynC7vDwY8vP49M48Ndt/2dfZtqqClsZeEdw7uJkx2tJp4rr2dTiwl7L/puUYgfqyuWsSBuQZdRWlljGU22JjJDMru1FUUXRuNedLpYFAp38TRLhx21TtFDIDhdThrMDQTrgg8fcznhrSnQWgFaP7j3sPFZwvApyBQKTK2GLoHicjkQRTtyee8hdLlMTqTXsUPmNpsNhULR5xy16HCBKCIcskRPPG8cyqVKtpxzAy6HE8WRK4TOer5rs/3rAjp3HcoFuGgQmmRfWutq8Q4KRvgN5sNnTYs55T4qVpYTNa2nGCzKNrDh84MkjAhi0qKeOVkmk4na6nKstRWEJqTiHxGFy+ZEUMp6EYqnbmVuampHVlZHbMbhGi4Oux254vD7rtXSSoO1jlDDQS6JHA5T3HllgiBDofDE4WhHLu9beDxVUsvHte6cpbzxqX2uUjkaURQRbU5k6qM+nvZ8Cd/9zb198SeQsgAAp9PKqn3FfJ1dBcAdX+zm7SsP/Z1tew1+PpQP93Az9JGArlarmTSp+1L4J4prWIs3P2cMotHmjugFH7V82GjaT0XlOwBs2TqF6dOKuo4dWQE8ZfRg9q/LRXNoCqmlroN966pIzAgmOPbwB7lcEBjjrWdbWwdjDokTALmnCrnKimvDawghCQhDD7tHW0taeUTQI4/2Ra05nNtirzYh81Ch8On781UQBCYmBzL+Xn9e+CYfxZp63m3N5oobxhIREcq3//cYPz5xL1VX386cmb1P00tI/FqckECZPn06eXnd8xKuvvpqkpOTuffee4mLiyMsLKxLqPxCQUEBc4+RP3Gm8ObrH+JprGH4DQ+CTWDLt0UMnhBGRJI7f8Elivy7pJaXKwwM8dDyWEIYwzx1aOQySjutLGtopqMjn/0N7QwLHNYlTgoLC/nkE3eoPPrmaAICDidDlpS+QFnZywBMmbyf7GU17FziFhlHu6jetvY21letJ1QfyooLVrh3ii4wt7m3zc0YytvZ/HURkSl+ZMyNRiaTI7rcUQGHw8iOHWdjtlSQmPggUZFX93gN7IZOHE1mNEl+CL1MLRUUFPDpp58CcNddd+Hp2b0irbPdRv1LObiMdvwuSUKXHoQgk+Gw2fngrptorqli0uXX9GrPr0nwoTOrHgDtkABWvvsKe9e67/OuL5b0+jsDcLa10fDSy6iio/H7y8AklZ40fcQjc1dVYDbayVtf3UOguFwuvnj7FXwHfYeXpoHtr4SzcOrbWNa4X4vwpyb0Gs1q/vAjbOXlBN56C3LvE7N7l8lkuMTD0aK9a1ey/PUXALj9k++RKxT4aHz418TuNviOpibkfn6MzPwWU0chvj6juo51ZmUhOl3oR49CFEVitIcFia4PgWkwGPj4448xmUzceeedeHh40PptER0761AEaQm58whBf+RrcGjb4TCyffs89NYabk8ahXGtQKDZG+dlw5ArlFB6ROHEI75MfFXwFQXNBdyQfgN+mu55JzaXi6dKanmtsoGH48MY6qkju83t6tzucHZrq9fF4+WVTnt7LkNSn+9xf/fGhvQ61bvuk4PUFLaye1Vlj7/zT4bF0WJ3dhM4AGx9BdnmQ1ForRckuJPQ1XE+WCvawebCnN/U9dqowjywFLXg9FHjNHZg2XsQTVoK2tSQHn/bMpmMOy5M5U6dQMTP9Xz1nyzOumkYN/zvRV556CH2vP0faiorueaaRT3uRULi1+KEBIqnpydDhnTPjNfr9fj7+3ftv+eee3jkkUcYNmwY6enpfPDBBxw4cICvv/564Eb9K7D/YCnGTT8iJo1j2uTRLH11D0q1nBELYynutNDmcPJdfQtvVzXySHwYf48MRCYIbKreRKco4iE6udRHSfqgKeiV3cPvdXV1Xdu1tbXdBIrVUtu1LYoOagr7do/d33TIG6Lj8DnIlXDVEqjcDsMuIfu9MmoKW6kpbGXY9EgEmRyX0/3tz2ypxmxxR7KKi//bQ6C4Ou0YXt6FaHOhCNQSclf3aA9AeXl513Z9fX0PgWI3dOAy2gH38lu3QBGwmEw017i/4W74+F0GT5rPjy/k0lzTwYX3ZxIY6YmjxYJ+fBjes6ORqeS92uD3Rstnn9HysdvRVubpgc855/TrvF+FPmYRhk6JoKn6APHDexbDczqc7AkazBdad8Lom2mX4Sjvu8wBgLWkhPpD06gtn3xywpWIZYIM0XX4gV2am9217bBZkfeSG2J45hma3nJXYU45kI+f6vD7uDNnF+WX/4WSuFhaJ44kZtJmkq2lfB3/EsPDZ6NX9B65OHDgQFdifF5eHmPHjsVS5P4bcBjM3dqKqedj2rILl9wDr0FzEQCbrRGLtQaA1MidbI67nP1Vzfx7TyEaX19uHfMg9rZMNJkpKOTue6o11fKvrf8C4PODn/dIBv/HwSq+rm/m0fgwrj9UvHCQXoNSENjW1sEQz8P5Y3K5lpGZ3/T1MnNHTO8uugGRHtQUtvZ6TC+Xd3kldSPwCD8Y3+5RNHVUz3B6YVY9TVUm0uO9afjXnXRu2wpyFdGfrgRBQBXl1SO6cuu0eBY4zNy0zcxXT2cx9+9Duf+FZ3nmsadpWf4J/1dZyZ3/vBPlGZI7JPHHZsDfZbfffjsWi4U77riD5uZmhg0bxsqVK4mPP3PnMF0uF188/wwKhZZb7rqFoiwDZXlNFMwN4rGcA1iO+CB/ND6Mv0UGsKVmMwARHhHEeMccs/+RI0disVjw9/dn6NCh3Y4lJt6PXh+Pr+9YFAo9Ey5MZM+aKhJH9rSi/u/k/7KqfBUXJ13c/UBomvsfEJ8RSEluAxq9ErlCQCaX47C7c3w89EkkxN+L2VJFfNzdR3fv5tCtis7eQwFjxoyhs7OT0NBQEhJ6Jg2r43zwnB6FaHXidWhJtiCXo/XyZtLl19BcXcnES6+iprCN5hr3Q3jbDyXMXhBD+3K3+GktbEF5Yxqzb7iNPWtWMnTKsV1TtenpXdu6/tTHMbfAe/PAsB8u/QoGHb9kABxePXPM3Jw+IihJo0P6LCypUCjYqzmceNmQ9QR+dw/CuK4S3dCAXq+nDA5GGRWFvaKCwNtvO+7YHS4RxRHfmmUyoVsEZdyFl4IoEpcxCrWu9/yWzl27+r6AACadHztHjsRT1UiItRQBcJb/E330PLKWfEf20h+YtOjKbuUN0tLSKCoqQhAEMjIyAPA9L5HOHAMe48K6XcK0YQNVz3/v3i5sI/w//0GniyU5+d/k7F9H7lZ3pHNd4GTavyzFMtvGiN02kupGQRlEHNLbflo/4rzjKGkr4eohPaOI65qNDPXQMSfQuyv52VMhZ4qfJx9UN3JVWEC31/JkmHBhImlTI/H07xld6ZPUcyBiH2h8unkE9UZHq5UVb7t9g7auySVRryVKoUDpsKEbFkRdUQG1SzYTkzAcjY8nqlA9cm81MVo1oWEeFF3kx8S1Lfzw/C4mX5rE/U88yOuvfkDHxm958o46bn38Mfx8PI85BgmJU0Wyugfefe8LWn7+iMQr7mT6pPG8+/A28gPk7JsZwCUhfqR5avFVKvCQy/BXOFhetpw5MXOOuRLnRHC5XLS2tuLj4zPg3gPVRbmYja0kDJ/Sr/amqnYK8htJGB+Bl27glhgebXdvszhY9/EBTC1W5l4/FJUI9S/m4DLZeXSIhiXhSl6VtbNnrdsv45FHHjmmMHCaOpCpVf2z5i9ZDx8e8lLxiYbb9xz3lI7WFj5/5B+01tVywYNPED00vdd25SvLiZ7ZMwfF2WGnbVkpCn8NnlMie9zL88/uYL2PSGKplZsvSSUq9fhmWaLdjstqQ36cOjHf1rdw4363+CufnIZaJsNhslK8t5CkMf3zCgGwHDhAy6ef4X3WAnQjuyd6i6KTVd9ehsx3J3vzprJz8CRWKdJ4Ot6Pq6KieP7yc3Ha3ZG1Y03XHQtrYSElF1yI024n6Mkn0M+ahcPhQKfTkddu5KZVW7Hs76DTocDqVGOZHc6KUgG/gp4J1w6XA4vD0uVIvGHDhq4clMGb8mi2u6dyApQKFoX6cXNUELlGMxfvLuafcaHcEn3itWz2r8tl8JT0k7r3E8VidXDeN7kc9HHylx0/84tyfvDWW5H5ePPqXy/FeqgY6a0vfoFoc6GKdAuOW/LLKem08mN6Ahs+L2D/xhrSZ0Qy9rwEfvxpNfmfvoJV48Oifz5KUsLAVQqX+P1zRlnd/xFoN3ZSu/JrnJFpnD1/Gs++kIXL6SL13EReSgnt8h8wO8xsqd5ErcqDcxLO6covcdhs2CxmdF4nlgNwJN9+9x1fNhoxK9V8deXFrGs20ulycW6QzymvpBFkckSX+8NWFEVWrVpFfn4+CxcuJDq654fLPzcV80NuDazOp+zp+V37W1paWLx4MV5eXpx11lnIewtBnwAqjYJZ13Z/OIbeO4q3KgwsqXBPiVWXlPR2aq8c7yHdjehxMPI6aC6Gc9/o1yl1xYW01rmn1jZ99kGfAqWv31bH9tqu/Bq5XoV+VPdoyk03ZXBpXQeBkZ695v70ei2lEnk/BNmqpsPeQu0OJ4Eqd+Kt0+mksLCw23vsmMulFQq44i90ABQVkVvRgtnuZEycP1ZrLW1iFTSH4udfyoH2C4mgnpeb65lgsxE1cQaF2zeTMnEKRUVFvfd/HERA9v57KGUyLL6+ONrakMvl1NXVMSIpiRfCq2nzfQGZwkJ4zINERM1BPwF2rNyCb0zQMa+r0RyOZGwclUK9zU6d1c6a5nberW7k9coGXIce8prfgYFZpcNBTqgChRM6VGr0NgtxcXEo/PwQRREPP3+snR3ovH1wttlQxx3+/Bqk0/BTQxvv1zZhm+CLt4dA7s+VtNR1Mu+vUwgPD2PZc0/w9SN3M+76fzB14gCvSpSQOMSfXqB8+fn3qJwWFvztb/y8sxp1fjuys8K5YfDh8LLdZWdNxRrmxs7tEiYANouZD+6+ifYGAxnzz2HKFb07Z7obd0LOBxA0GOIOO3FSlQ3Vq9maejV2mZJJOw5QYbEBsLKxjddSY7qa1phqWFe5jpnRMwnUBfa4hCiKGJevQO7ni37UoeRFga4kWaPRyObN7qmp9957j0cffbRHH+1me6/Dz8rKouSQYBg0aFCPlVpHUtBhod3hPKpM/fEDdYJSxtWxwXholcTq1MQkB7Nx40aGDBkyYEueAXfezvz/ndAp0WnDGTZzLqaWZubccMcJX1Ide/gBoE7w6XFcqZYTFN33Nw67vQWbrQW9/sR9Wm6LDqbT6WSynxcBSvefvCAIJETFdX1rPlF2V7Zy9wp3MvzcBiWvXjYeW1EKbdptmDfPJTziALmpo7jZ0UpCQgIJCQk0zr+S5hoTsbFByHvz6OhoggNLIGE6eB+7LMGR/JLDkpw2hp1Z/wdAQ82bJMe5p2/8EkO6eTDlGnJRypWk+h82GywsLOza9lcp8FcpGOyhZZq/FzdEBnHPwUr8lAoeSQjr92qk49Fpc6Dro6hgR0cRu/dch8NhZOyYVSiVPifUd5xOzblBPvzY0Mqsq64mTQ7Bwe6ojyAInPPI47y27hnUkWEownVYDjYjOkU0g3yZHeDNUyW1PFpUg0om0OHlIm6iBxdubab0ye2cf/Mwrv6/53n7kYfZ8coTiK4HmDa5p+mjhMSp8qcXKNV7d+HyjiBlUAzfPLYZMUjJP+d2N5TbULWBWdGzuokTAHN7G+0N7mWx2T99f2yBsuVFWHcoA//a1e4J8Y4meG8OFzhtTN32E6njfmBOgBfvVjfiEOmR/X/72tvJb87nqR1P9UjsA2hfvJiaf9wLQNj/PY33woXIZIquXAMPDw8GDx7M/v37Ofvsw3bx1TVf0NaaTbH1Kpo6bDyYXMclnruhcRAEuF+LpKQktm3bhtPppMyq59qnVjMrNYRHz+7uKFtmtjJ950HsosiiUD+eS/7FXbV/AkMpE7g07ND0ho8HF110Ub/OO5KcFeXsXl3JuHPjSRrTP1Ot46FQKplx7U3Hb9gH6lhvwp8YDzKh3xGSX3A4jGzdNgu7vZng4LMZkvrcCZ2fpNfw/tCBrdKsVytQygXsTpGYAD0FhU9itdcSX/QfmBTAvGUvMmnnKv7+2gcAWM0OvvlPFg6biw2fF3Dts71Ut/7+Bihc7t5+tK3fY2loaDjk5KpFq7kNh7OVyIhLqK+vR6VSdfNZ2la7jetWuGs/PTj6QS5OdudzHUsAh2tUfDpsYHPoHlu8j/c2lxHtr2P9PVN7HDcYlmE2uxPa6+oXExnxl27HRZdI04f7sRxoxnt+HJ4TDzvUiqKIy+HgtdQYXgPIes8t/GY8BiHuqOXymlV82rIYWsApOrk54QbM+xsR5DIGaTTkjR+Ct0KOQibQZHOQ1d7BuphG9N9W8tlTO8n4awr3PP8c/73jTra8+QxxsS8QEzUwf2sSEr/wpxYoTocTZUMp6rRJ1LaY8a21oj07slseiMVhQXTaUTqs7m/eR+AdFMLs62+jqbqS0ece50HqecQfr/aQ7bpcAQotOG34BMaRo7djvPQ8JgeFEPr++wwO7L780Ut97Dk94YiiazK9O3ohCAIciqDgcDB66TKGbttG+CGbe5utmebv9iNWT2CzcgXN9kDOVz6Mh2CCvA+6HhRRUVE89NBDAFz0+lZq2iy8v6Wsh0CxusQub5h6a+/RGFwuqNoJ/gmgP7nCZEUtRWys3shZ8Wd1q2EjiiLbfyjB5RRZ9X7+gAmUgUA4TpmEvnA4jF3VqBsb1wzYeE4l/SwhyIPlt0/CYncR56di3frvUPq3U+h/M8OrH2PSBrcLscbpfu8JAsjkMsCFxqOPCITq5HK6xo0b17WdmJiI0+nEZrNht9ux2WyEhR2Ohtqctq5ts+PwKqGBTMUTRZH/7PwPayrW8NTEpxgR7PaNclkPV8veUNAAQHlTZ699hIQspKFxNSASGtJzSb6rw47lQDMAbT+VdAkUURT55JNPKCoqIi0tjfPOmgs/3em2Iyha1fX3nBaYhlyQ4xSdzPaajq3GhMeYw6+T/xGRHX+VgtkB3swO8KY0KYSPXt5F7hv70d4ylL8+/Cgf3nsr7z3xGPe/9KJkjS8xoPypBcrOXftROy2kZowgb7/bcj097fDUSVFLETUtRUz58R63EdrMf8H47ismhkyd2b+LjbjC7X7pFQb6Qw9UjTfcsAlaK5BHj0f276dwGY2EGY1EFR2EwLHdunhx6ovsadxDoiKK0l1ZRKcNR3ZELojXzJkoPv0EuZcX6kMrbASZrOvD11ZaRue2bQBU33EnXnPnsqV0FwmVM1ih3I1GaGWGqpUSZyIZ8l0Q3HsC5WVjosipaGFKUs9ppiS9hq/T42myOzgr0Kf312LTM7DmUA2nf5SCzq/3dsfg5jU3U22q5tnsZ7tFkwRBYNj0SHatqGD02T2jBiUlJXz44YcIgsB99913xpgEHguNJoxhaW/S0VlMRPjA+LyIIseM5JS2lVJprGRC+IQekcNfiAv0oLOzkxdffJHOzoXExlUxb+5F2HccXu3jbDeiCAhApVFw8T9H0lrfSWRKH7/vha/CsEUQ2b/pgl0VLVz/cTbeWiU/3jwBzSFjQLlcjlarxSSaWFu7lqmRhyMUE8Mn8sr0V1DL1YwO7f06TlGkweYgRH1yUzm1HbV8nO9e9n7lz1d2vT9lR7gJP37OED7dXsGV42J67UOrjWLUyO/7vIbcU4XXzGjMB5rxPefwajqn09k1Fbtnzx7OO/dcSJwNBctg1OGyFyn+Key4bAeOqg7kyFEn9C+hMdZfz23/GMWLT25n/dv7uemRsYy97g6yX/s3zz/1HPc9em+/+pGQ6A9/aoGSvX0nDkHOhHEZLPm6mBa9DH//w1GIFmsLI9T+0FxJaacvISv/g3b8bRgtdrRKOYqj5tHNDjP/t+P/sLvsPDTmITSKI6ZoBKFrKXA3fKLc/wDfiy/CvHs3yuBgdJk9PUh0Sh2jgkby1k1XY2ppRqFUcdvH33Zvc4TL7y/XFQ9N8agTE/C9/HI6d+4k/H//xSWK/Hf3W1xvH0WIPJAKuVukBdkfxXlTAvKQ3vMAFqaHszA9vNdjABN8j5PX0NF4eNth7bXJS+X1rGlu59+JEQwSFNirTajjvLuiEIHaQKpN1b2eO+68BMadl4DV7KChwkhApEdXCD8/3+0XIooiBoOByMhTKzT3WxEQMI0ABrD+jlOEPgRKm7WNi5dcjNlhJs47jh/O+aHPbkwmE52d7ihAY0MKgYGzcN48DkWAP5qhaajjDvt1eAVo8Qro3cEYcEdQBvVePBCg1e6gzeHsqtr7bU419e1W6tut7K1uIzOmu/C5e/3d5BhyeHzb410iQRAEJkVMoqPNitPuQq7sKb4u31PC2mYjSXoN60cl9zhut9v59ttvKS8v54orriAkpHvCc4g+hJnRM1lZvpKnJvYs7wEwLj6AcfEBvR7rL17To/Ca3r1ApUKhYOHChRQUFDB16lT3586ln7sdpw+56FoOHKD1y6/QjZuHNi0FZdCJRa68tSpmXTeEDc/k8tkbu7nmztEc3Hsu5o3f8OnnKVx6Sf+rjUtIHIs/tUBpOLgP0ScCD70Wa7mJ8kAFVRZb1wegTqmjRqWgVHEJeyvdJmPjVhSzZ00Za3HwwgOTCfY6LEJWV6zmm0K3aZPVaeV/k4+fiGnvtFO6p5GQBB+8EhJo+d/rPLeygAW5tVyU2XM5qiiKOGzuMLXDbuuty27IBFmXQBFkMkIe/GfXsdcqDOTpZ/JWwIdMzvXEWyHj8qgR6Ob6IAvqKUDq2ix4ahToD1mQu1wOBEGG0Ms3bEOnAW+1N2p5LxGKaQ+CXxxEjASvnlMwjTYHT5a4V8xM3XmQ3Fwnjnr3Q/CXpaJvzHyDwtbCbomOR+JyiXz57520N5jxDtJy+b/c0ahRo0bR0NBAYGAg4eF9i6xfg90Nu6k0VjInZg4K2en90xNdIoK87wjKL1E3q7N3AfkLQUFBLFy4kLa2tq6pFrmHB/7X9szH2lPVylXv7cTmcLHl/ml4afofoWi1Oxi3PZ9mu5Mrwvz/n72zDo/iatv4b303m427ESAJhODuXtqipUpdqVF3d3c36kILNYoVd/cAIZ4Q4r5JdrM+8/0xIZtlN0FKv7fvS++rvZjMOXNGdnfOcx65b17rFs9lg+LZkl9DhEFD77ggr2PaowHI2l7Omq8lQ/WG10eiM3iGJfY1St+1bLPV5/GlpaWthu7333/PAw94cgrJZXLeGvvWSd/bmUafPn3o06eP5842FP/lTz+NNf0A9Qt+I3H+KgSTw6OK5xgKqk3kVJoYl2JAo/askhvSKZhPJoYxYFk1f/yaw623X8vzBdkcXfgVB3v3oFePEwur/ot/cSKctQaKw+lEU1OItv8ErGYHpvJmigbp2d9kYUSwgaONRxEEgZTwbuQEdgFK6BYwiPi1ZcSjZgpqssobWw0Ua3YOvYUIwnRh1FhquLqHZ1Jbo72RbzO+pXtIdyZ2kiiqnUYrq57fSaFZik3f8v4Y3lqZw+a8Gjbl1nDZIM/VkavRjq2ogcuefJWKoly6DTtOG0MU4eDPoNRAjxmAVGbcSvFdmw/7voe0mRDdmzpbE3a/AewcOoA5U9Wc26sHjqpmqj7cT/2fm4m4qx/qGIknYvmhCm79XmIbXffAWMK0xezefSGCYGPI4GX4+7vp2xfnL+axzY8BsP7S9YTqjssz0RhgSPsqyyEqBeNCDKyra+KxLtGI24u8+vip/Ig0JdJgtxIa601aJbpEmhukybWhDSNpeHg41113Xbvn/rtQ1VzFdX9eh1N08mn6pyyeubjD/oIosrexmSQ/DUGqv+FnKrTvQQnUBPLDlB8oaizyCI+0B5fLRVZWFrGxsSQnJ3Po0CHWr1/P4MGDGTzYTYW/9GA5dWbJqN6cW8PkXp7GqegUEKxOFP7eeQxGp6uVm2RJtZHXusXTMzaQtfePbfe63hzzJnsq9zAgcoDH/uqjTa3b5gabl4EyNy2R5TUN3BTnHcIEiImJoWvXruTn53PZZZf57PNX0FBViV9QECr13xN+1PXqjTX9ADit6NLCsFY0Mm/JF+T7l3D/iAdxOFWszqzkid/3YVMWoHIF8+X5PzBi2AqPcZ45J4W3quzI1pSyIs6PO596nPfvup1f33iZrh9+iJ/uFEjo/sW/8IGz1kDZvusAasFOr4H9JcppEY6GKxnWUhp7pPEIo+OkSoOJN92O028NnaqU4H63MTpZeoGZtmyh+EZpxbjw3XcIuGSSl+fjq0Nf8flBiSb82/O/pV9EPxzlZnAIuGSwvpeOqsJyxveIYHNeDUF+Ki9OiurPD7TSf/d8ZaL3TWUvg99mt1zcgzD+CWQyGUJLoiKL7oKizbD5LXimgWnCD1jFEhI4wojO7wNgO9KAaJMmAvOuCtQzpJVQdoX7xgtrTKj8dyMIkgFQUbGQpCQp9lxqtfNHsTsHoaq5yttAOQHkMhk/tqmacM4OwJpvxK9NflDx4ToWvbcfgPHXpJI63HOyU6jkzLinH2V5RnqM8GQk/U9AKVeiUqhwOp1E+p2Y5Ov1wgreLpJ4UwpG98bvuHCiIIhUNdmIDNCcVgm26BI6zEFJCU4hJTil3fa2WL58OU6nkx9++IFnnnmGtWvXUldXx7JlyzwMlFmDEthZWEewn5oJqZ6U/6JLoPK9fTirmvHrH0HIpZ56RYk6DZ+nJVJosbVrOBwPP5Ufo+Lc5Gz2oiLsxSUMOHcAcoWcqM4BhMV5hyNHhRgYFdJ+mFKtVnP11Ve32/5XcGD1clbNlbS5bv98HjrDqZNdfbA2lzdW5tAnPog/5ozwao98/DFCb7oRZUvZ8T4hgzerPySpOJ7n9n/C1POv4pzUCJ7ZvARl4FYcTT3IrTZy/EiddBpeubYvr1XvwPpjHr2eGMw5cx5g81tP8u7Lb/Hoc4+d8rX/i3/RFmetgbJ/x24cMiUjhvVnz+IibP4K9CEa+gX4kVGTQYBaejEYm+28t/YoyoDeBIf+jG6+EaHyKIHThyGTSy8/V72xdVxXZSWiaCc//11kMgWdO9+NXK4kKcjt8ozWS5OpNiWEoeclUGQ0sTVaYGtpDeNCDBS8NBm5r8njRIUGfm0MgRatDplcLmXwA4SnSAZKC8IDezO29BsczcHk75KTPMCOX59wmrLqkavkBE11J5neMDIRp0ugs07NuJQIXMIMmpoyUCj1dO3qdnHPSs8nzzkOP4OZTwZMJDU09SQvvn0ow3T4H5e74LC7Rdsceblk3zkNwWym66qVqFvySqK6BBLlw3X9n0CINoRfp/1KRXMFAyO984uOR7XdXfHh9FFhcs/8/SxKLyNQpyL96ZOj6vfCGeKW6du3L7t372ZIS2XYgAEDWLVqFWlpnuG3NeU/Epmym8cHP47mOG0ewerCWS2FVpr3VnkZKABTI4JO+xpdjY0UXngRgtmMpls3Rvyx8LTH+jtRU+z2FlqaGk/LQFlyQAqPphcbfbbLZDJUUVEIVieNm0rpEhJJqCGMw/ICJsbMYXDnENRqJdP7JLDoyFaUfnkEx7zJsoPl+LWouosiGLRKuob7c/0tfZn3wk5+/fQAdz42hANjL8K8/mcW/LKMSy+efOoP4V/8ixactQZKbe5hxJAEdFoNhw/Xkh2q4Kmu4aw9upbOQZ1JC5Rerl9uLuTLLYXAWLrPnMTwuQOR2V0o2wj+BUw+H9HpQBEQiGH8OCoqF1N0VGIotVpLSUt7iyldptAvoh9BmqDW2LhMISNkchemWGy8vyeHOoeLG2LDfBsnQPhNvbAVNqBNbccjkTAU5uwEuRJCJQ+ETCZHPGYcTH4ThtwKIVJbdNQF6Ir8WfprI/muYjbPL2b6XX1ZtFVicp3cI5TOfaTVqkGr4rp6Ecu6EkqXlRD3yihSU70TAFUyGaLCgDn4cs7p1NejzWRzkl5sZGBisNcEdaro3CeM82/thUqjwG/7IqrMEm13w6JFhM85fb6SYygpndciqHgDnTvf+ZfHA4gPiCc+4OSScp/sGk2yXsPQIH8CfDyr9BIjAA3tEOudCFL5+ZkprZ06dSpTp05t/XvEiBGMGOG53q4wV/DuXkktefLvk714fBR6FSGzumE/2oRh3JlPXBZdLkSnZPS5Gk6eY+V04BBEviipJlil5NKo4FPycA29aBZqnY6YlFRCYk6erK4tHj6/Ox+vz+fKIQkd9mvaUELTumIAFt70E+ouAazfUIyihWbhmZEPcU6XEfQI7UGEn7fIZWWjlSO1ZkrqLVRNCKfzkkp2Link5luu5rmsg+T9+gXZfdP+pcP/F6eNs9JAsdrsaGuL0A2ahNnswFhTjWVYDSFOkWHxYzwSGPvEB7VuD04ZgSqgJfFOECRmWIUKWd8rPRR0Df5pKBT+lFrNOBTdMZZuaW0TEZH5IC37KFFAFEFjy2BbmQyZTIYciZJchgy5rGU7Roa8saJ1n1KupFtIm9VmuOfKUyaXtzLJIpd7tdc+/SYETodg6T6bm9yJt421nkmCxxJVO8L8vl3Z3WBmTIj3yu+Gr3ax84jE3dCWRv90IJPJ6NJXMp6ckdOx7N+PXKslbPbsvzTuMRQd+Rins5GCwnfOmIFyKghUKbkl3ntSOIa3Lu3LH/tLuaKdSUgURTZv3ozRaGTixInodMdVz8hliMdCf/8PCNWF0j+iP3ur9nLfgPt89vHrE4Ffn/bv+a9AGRxM4o/zsBUWEnBu+5VCZwK/VNbxTL6ksFxld7Tq9giCgOB0gUz6fSPzJojzCwhk5Kxr/tL5x3WLYFy3Ez9HVaQ7iVgd6odSrkQURRQtlXIqhYqx8WNb+1izsih78CGUkZHEf/QhkQFaIgO0pEYHsK/RzLo0LfLlRcSmBHP7E4/zyX1zmPfKizz64Qf/8qP8i9PCWWmg7Nx9EJXooNegAXy1fRUN2gpuHjyTkT5i2xNSI0l/ahI6tQJ1W6KtnD9hyT3SdsVBOP/V1ia9vgujRu5ka/k2RsWNPaVrE0URQRQQEEAEAQFBFBBFERGpTURs7bejYoengXIcZG2qeHzBb9hQei/4DFPnQQz87WMUSjmCS0Djp6JL33AsBw7grKrCf/x4Qi7rRvP+Kvz6tf/yC1erOL8d/hOj5cRVR6cDZUgIce++c0bHTEi4kdy8V0iI91a7/buw6rMPOLBmOYMvuJSauloio6IZduFlPlfgAzoFM6BTcLtjVVZWsmbNGgDS09N54oknPDvIAaf3cX8XVHIV35z/DYIotMurcjx2Li5g19Ij9Bwdy5gr2nzH9/8IR7fCmIdPiRJf26MH2g4kGk4WDocDVQcaSIk6d3JrH4PbCNAZ9GRvPSjlrJ+y80oEZPxa2MTQTqdQfSaKHYbyhNGRyFRyMveVgSiSebSB9pidjAt+xpabiy03F8uBA61UCFqVgqv6xjG3rIbgCDmNXx7grieGMerme9n1wfO899r7PPTk/Sd/zf/iX7TgrDRQqiolevpuyYksW5lFpCOZ4V08wyY1lhpe3fkqUfoo7h1wr/dLNaBN4mVMf0z1NvatLCK2WzBd+oajUGiQy9wvsaqqKlauXEliYiIjRozwLh8WREzbypAp5egHR6GQebv1syua+PNQORf1jyM+RHrx+avcFSyC4OLA6hWodTpSR46VvDByhYcrv6qokeZGO516hiKTyYh++mki7r8fhb80jsUl4NcvlE46DfaSEo5cfgW4XPgNHUqnr79CPymeD/d/iLXIyt397/bkegGwNsL6VyAoXgontbnPz68ZxLrsKib3imb/iqWs+2YufSdNZtx1N3sM0djYSHZ2Nt27d8dg8E5WNNmcfLvtCD1jAhmd4jthMnfHVuxWCz1GjZPycE4R8fHXER9/3SkfdyJYmuys/joTq9nBtDv7oNVL3xGX08GBtVKVxM6FC2hKHQjVDTgPP8voJ5855fMEBQUREhJCXV0d55zjY8o5gyEeAKpz4JtpYKqAuw9AsKdb3+lsovDIB2i1scTFXn1SYY9DG0tb/201UKwNEiU+Iuz9tpUZ1Vlvpf7nHGRqBaFXpoJSRuOSpQhmE0GXXIJMcSx3QmRt8VrCdGH0Ce/j67QdYtmyZezcuZPIyEhuu+02n32GBfmzZ1gPNHI5YW0YWbsO8uZUOVUcIJ1zxp5Z2YK26Ih2MnD6NBqXL0eu16Pt6Uni2MWg47fzenNveD7ypVX8MvcgV983kIN7puLYtog/lvRlxtQJf9t1/4v/TZyVBorVIoUu/PV+2CwObHqlB709wM/ZP7P8yHIAugR2YWbyTM9BYvrBPQdBpoDAWHZ+l0nmlnIOrCvhmpeGYwjRunM/gM2bN5OXl0deXh69evUiMNAzedOSUUPDYokB0lnVTNA0b+2P277fQ0GNmXdW5/oMkeRs28yaLz4CoL68jBGXXgm4idoaqpv55dU9iIJIp56hTL1DekEfM05cosik3dnkNtsYEeTPTxE6KSzkciFvCRFsK9vWWo10sOYg30/+3vMi9nwN2z+UthUqGOTmw0gI9Wtlzly0bCGCy8nePxd5GSgLFiygpKSEpUuX+hQ0fGZRBr/skXhpltw5kp6xns+yLCeTRW+9BED+nh1Mv++fU02Qv6+aoxm1AOxYVMCYy6WJV6FUMeryazm0biXVBjfxl+7nX2k+bwp+g05NMVar1TJnzhwEQfC52pfJZGfUPiFvtWScAOz7TuK6aYPS0h85elT63shlKmJjL29tq7PW8fqu14nWR3Nj7znclXWUPQ3NvDKjEzUrSul/XhtjR+0Psf2hdA/0c1fSNO+pxFYgGSvmfZXIlRWUPfggAE3r1pHwqZQTtih/EU9ska7t7bFvt5b8nyyOiQpWVlZ22C9W6xnSqK5eSXHJd3RKmE1oqA8dojaw2ao5cPAWTKZMhg5ZiU4n5eScSTr+04Gub19Stm5pt31QoJ4fh3fnploHBzbUMX9BJnPuuJHncjM4NO8TevdOpXPCf76i7l/89+Cfrxv+N8BukwwUp8KGv0uPzYeZNizGTTM/PGa4dweQGGADJXdrWyVajZ/3gHFxccRSThANFBV583oog92eCHWi78qThFBv4qm2+SyGULc3ISKxpYpHIUc4loPSprfcB0mXVRA40hKG2WI0oYqNpfMvv5Dw5RfEfSQZHcnBya3aN9en+Qh/xLZhsu00kuxtm5h7x40UH/ZMihw84xK0/gaGXjTLawittn3+BKvDxaL9bgbZEL13bFuj90eukD6D8ITOXu3/SST0CCGoJfbfd6Jn/sjgGRdzwzufcf4Nt9HQoGLiipX4252oKT6tcykUivZDETLOrAel1yWQcp5Eqz7SO8fEENCrdTsw0JOX5Ofsn1lSsIS5B+fy9oF5LK1uoMLu4AWVmXO7ZJH99OWsnTwRNr8NdhPmyz8lffIU8np2ajW+tT1Ckeulz1zXIxRlaAiyFhkDv/7u86nk7uehVpx6XsSUKVNITU3lhhtuAFpCsoLrBEdBds5z1NdvZX/6iUOGtXUbaGxMRxDs5Be4Cd/+0wbKySBeq+aqAXEcSlCze0splYVN3PzE44gyOV+/9CIO5/9jXPFf/NdDJv7DvvWNjY0EBgbS0NBAQMCpl9idDD75+BsaNvzGiDcfJPMXNYfrbTz4yBBijlv1CKKADNlJZ+E3N9rR6JWtUvKbSzczMlYiUytf+R7RWyWxvRWx93Hu7Ke9jnc12kAmQ2Hw/eK0OwVyq5roHhWAoqXSZ0vpFkbEuismGqoqkCuVGEIkI8LS3EjmhoX0P19KvKsubsLSZCc+NcTnfa2tbWRHg5nZceEe7um2cAgORFFs/wVvbZTI4pQavnnwDmqOHiHIHsWNv3/uu/9xsNlsFBcXk5CQgFrteQ67U2DUa2upbLQRYdCw83HfK2BjZQUuh4PQuP9fKvujK4tImHRmqhaELy9EdmQNMjnwtPGMlQUDuMwOXPVW1HEGRFGk4c9CLPurCb4kBW2yd25LZdUyMjMfJSxs/CmrKR+Dw9GIXK5B0YZdWBRFDuxexrUHH8alkPHbjGU8Umhji9HEXFcBaWuf5WHrNZS5DDwg/4mZgXvInDWHsrL5AAwevBSDv+/QiaOiAtFuR52Q4HG+nRU7CdYGe/C8ZGVl0b37qYVgXE4H859+hPK8bMZcdQMDp3mL+h1Dbu5LHC3+gpDgEfTr922H49rttRzKuBtLcxEDB/6CRiMl2bpcLn7ZnMFlYzwlM+yWZmqKi4hKSpFCuidA5tZy6ivMDDg/EY3O92+8yubgjswiTC6Bn/p09VlJ5gtLD5QTE6zluoJirt9kJsgkcNkTg9iwbScHP38V5YBzufehO05qrH/x34czPX+flSEeu82GS6bE7rJTrsijSm9j1VE7SXpp5d5epc3JQGxy23tt1VIjA9wT7cSRvnkwFAEdM0eqlXLSYjy9Kwq5gi2lx7ldHUBpNgBOu42Q/BJMmyT+E13L/+bN+MTglv8pzcfU4dWI2KUyBInwS9by/3H7+nbuzp7MTAyde1GUUSt1QwZyWiqUkCZgZICIKOUGoxVDqCwwIeUKi7T8hwyYOymNcqOVblGG1nCJ99WpkKHC3OC7/XRx7Bqg7YbbN+U6vjLGYQFVB/ozHUCeMhqOrjmtY08EmfS4ARBMDkwt+R41XxxqlRNoi5Lib3G5TFRWLjptA0Wl8n5hVb3yCupvvuVHoHvmYWQyGQ9Yt3FJZRFhGZ9wUIhgUOkGFAhs13dhSGAl4WHntBooSsIpzc4kOinFQzgTQHWcRo503zKfIoHO01jZm+pqKc+Tfmcbvv+yQwMlOfkxkpIe8SkLcTzU6lD69/ved6OP19KC5x6nskAKPd0/f0mHYzdUW1j7rUTTv2/lUeZ84lvfaVG1kY310hvg7SMVPJ3knZj7wvYXmJ89n8u6XcYTQ6WwWZdwPfXNdgaGGtg1Vs3436tZ800mU24fQcaec3DuWcGK1f04d6I3gdy/+BfH46w0UBxWKy6FivEJ4znQsI1wpcDVycORH7dCtTqtaBSnx9R5POSDbsJIAEWyGHp3H/OXxzuGodFDT9zpjr8mMte8ezeO8goCJp/fmmwILS7ntv8LgjTntfm7V2oqPa+5HrlWg0wplTFKzS1GR+u/bewbjpVgIhk74FGSmQD4kF38f4WH47HNprXtHLn4HtjzlZSvdPP6Uz/JyHtgwLWgDfLpPRFFkdwqE4mhes8Ks5OBXIbYEuKR+6vw6xtO8/5qgi/2zR4bH3895uZ8wsK8v0uC4KTJlIG/vruHd+RkYMvLb93+cWcx03oEs2KFlCwsk8cxXLaJXKTwkKiXI1g/RnckhQnjpeN+eOxeKvJPbnLuCO2pWouiSG2picBwP1QaTwMoIDySkbOuoSwnkwk3+E6YbYuTMU46gjtU6wlz/ckb4TqDCkOIlqY6K30ntu9dPCc0gC9Kqim02LmxHebeRfmLAJifPb/VQEmNDqC4rhl7cRVGg5IJ16Wy9MMDpK8p5q4HbueFOdns/Oo9eqYlExv995SU/4v/HZyVOSguuw2hJRZttbkI1Kq8jJNfc35l0A+D6P1tbxzCicmwvimtIWXTAd4orPDZ7kTOlPVRzPjdzIQ3N3i1CxYL5c88Q/mTTyHYfZfj1tZu4MDB2zAad/u+L5eF7JznyM17BUHwXBE6HA5+//13vv76a8wtpGbFWXXk7KxonagA7CVN1P+Wi62oUfq7uJiia66l7MEHyR48hMzuqZh37gQkd565uRmZQoFMqUSmViNXq8ncvpldyxcjqFTI9XoU/npkSskWlslklGTVsWtJIbZmJ0qVAqVagUoj/atUKVCo5CiUcuQKOZkVjVz/zS6+3VZ0UjF4URRZd3Qdm0o2ddhvV8UuXtj+Ann1eb6ftd3Jc3llLKoytjvGj1k/8umBT3GIDmRyyWvkYczmrpL+LdvnewCnDXZ8Bvlr279QXXC7oZ0Xl2Yy6e2NpDzxZ/vHtweZrFWjSSaTETKrO3GvjEI/0DcNf0TEuYwetYseqa96tWVlPcru3ReyfoNUwuusqaFx1SoEi9uDKIoiGYcfYM3arhQXf9O6P+qZp8mZeBF3jbmbx34/yN6y5laF6fAxN7Kp+VJcQXH8Oe5iSofdBMgw73T/xkzG+pO6XavZxMYfviJjQ8ceqWq7g89LqilslmQcdi09wvwXdvHZ3RtwOQQwVcH3F8E305DZzQyZeSkzH36agHDPyfbAmhXMveMG0lct83092TlYDh46qWs/Bkk+yfu7cOFjzzH6qhu47bN2vC5toNYqueKZIdzw+khGXJzcbr9OOg3bhvagYlxf4rS+Q7kPDXqIHqE9+GjCRx7740P8aJKDq6KZxF5h9D0ngW2/5VNX3Mx1jz+OXBSY+8JLuJwnzt35F2c3zkoPitNuR1RIBorMKSJXe9tpW8u2tm7bXXaP5DpfeP9oJY1OgTeOVPBAZ7druTS7HoVajsNZhby2GBRhFNSYvY5vWrUK40+S21owm4l9602vPlnZT2O1FlNdvbJ1BdkWVVV/UlIivfwdDiM9Ul9pbcvPzyc9PR2An376iZnnz2LRO/sBOLyljAvulZJb63/Pw1FqwryzgrhXRiFTq5GpVIg2G2KLYVP2wINovv+OL7/8EoArrriClBRp5V11pIDlH0khgMMb1nD92594XKPT7mLpRwcQnCJ7/ixq18V8DO+tyWV9djXrs6u5dGA8OnXHsfAtZVu4a91dANzW5zZu73u7z36PbHqEquYq5mfP92I1BXjvaCWfFlcDkKBNoW+AZ4Ly/ur9vLxTYtJdX7yen6b+5H2Sae9IFS3D2iF62/4RrH5G2r56IXQ9sTBfW/j6Hp00ZMAZ4mmz2jyN8qPXX48tVzL8UrOkcILLZaKi4ncAcnKfIz7+WgDU8fHob7+T3G8lo3tb7W/8oPiIy8+9nPHDxpH79NP0q65m+oaNxH6yAjHUhXpoCIfWryahZx8uevRZjuzfQ4/RHX+P9i77g12LJKVxnSGALv19V0Xdl1XMqtpGnqCUinF9aaxxG1nbDmQTkfkpKXmrpR2b34IJT/kcZ+uC7zEb61n9+Uf0OceT7t2alUXhhReBIBB66y1E3HNPh9d+DKIo+gw8hyckEp6QeFJjANJCQH2cJ9ThQKY+taThi1Mu5uKUi732m10uMnAyJdAPi93F0BldKM8zsvKLQ1z62CDSLr+F/O/f4eMPv+SOu88MseK/+N/E2elBcdgQlZLBoXKJNPlgTZrTdw7nJ57PK6NeQa/Se7Ufj1vjI1DJZMxJcK+kijJqWfj2Pn5+cSkLnriPC47+zL1VB9gw27sqSNe3L4pgKTkx+OqrfJ4jLGwsAAZDT5/tAQF9UCikkuGYaM8XR1xcHOHhkqv2nHPOQamWtzJGBrTRudEcq0Zq+WaoIiPp8sdCEr75moDp01B37kynb7+hvt69ci0tdVfV6IOC0eqla0gZ5p3LIFfKCQyXJvvopBPr5EzqIRl7KoUMlY/Ko+OhU7rvJUgT1G6/YxwYsf6+Sa96+7vHidV6G6fR+mgMaomjZVrXab5PknwOXPotxLdTImxoI3Dof+ru7udmpHHn+CQW3XHq8XxpIX5m8uN7pL5K1y4PMHTISgBEu7fHUak00DnxTnTaBPr2/cajbWKPSA4+M4m8F89n2dEfQQY/5vwIQNTTT6EfNYpO331DwNh4As9NZNUXH7Di43eYO+d6wuI7MXDahfgFBnV4jRGd3VpYHSVOhxynHD3ioiSSh6lYE1DDn4t+ZkWeSAMt3DwD2q/I6XvuVI9/20K0WiUmaiRv08ni76hmEJ1OimZdTlbvPlS/994J+++u2M2liy/lvb3vtevR/KqkBrNLoLdag06tQKGUM+nGNKxmJ+u+z2LG1Am40sbQvHUx6zbtOtO39C/+h3BWVvE8e98jCBYTz378AR8+sJFNiSo+uHlQu1Urp4sjB2tY+uEBBEcJdtMCABSafgTHnsdVjw3yqtYRXS002B0Qi7lctg7j/IJgB+TIW+j6RVGkeU8liOA3MNIjBGGsbMZmcRKZ6H7OoigimB3I9aoOc29cLhc7d+5Eo9HQr18/j75WkwmHzYohNMznsU67C3ODrdVQOREEQWxXn8gXMmozUMqUHTLsCqJAnbWutWTaFyptDoJUCjTtfB4muwmH4CBY6656sebUo01pn+HVC+UHwD8SDCdWOD6TEB0CtqONaLsGnbEx7fY6zOY89JYYLLv24D9hQivHzsni55yf+fLgl9zc+2Zv7qEWLHv/DTI3rwdOLe+kucGIWueH0oenIDc3l+TkZOyCwM4GM30NfvgrFRQd3M8vL0j5FbUxKagDA2hSNbEybqXkectdLamID5vTqn91MjBt2oRgtWKYONHjt+NqsFH16QFcdVbCb+vjXjAAzVY7f+7O4aKRvhcopwNndTW5o9y8LMc8Xu3h1tW3tiblp1+T7kFgKYoi7+w4wjvl1UyNCua2hAgPjqL8fVUs//QQYy5PIWloBC/PuQOFzcStb31IRPgp/Gb+xT8W/1bxnAGIDjsypfSSUrlEXEoZHxyt5Bkfmep/BYm9wph+V18U6v6YjansXlpAfWU4A0WB8hd3EDQzCf8h7lW0THHiUr4TJSHK5e6X76ZNm9i18w96ddlNoDmOpKNPEHKRu5QyKNIHr4pMhsL/xK5ehULBsGHDPPaJokhNiYmgCD+0HUxMSrXipI0TwNs4sTXBqqdAEyC52I8rrUwL9VTR9TmmTN6hcQIQqek4rOevPrnJVxAFykxlkrdGFKmd+zmCqYmwOXOQR596um/6qj9Z+9UnpIwfT+LMiaSGpHZIH2+z13Do0F1YraUMHPgrGnWYz2qQvwJRFNi1+0Ks1mKUygDGzGgn7+YEuCTlEi5JuaTDPufcfAcpw0YRk3JqZcEn8rIAqOVyRga72YstTY2t24ItApNNjVFmY/sV26WdP18H9ibY/UUrq+3JwH+Ut3cRwFbUiKtO4mlqXHOU8BvcxohHBdkZgjI8nMjHHqV5z14iH37ohP2ndZnGltItHursx7Ct3sS7hRX0jgvknqRoksI8Pc9d+0XQa0wsm3/OI7JLIJc//Bi/P3UfH7/wEk++/aoXWea/+Bdnp4HitCPXtbCnOgTGRATwUnE1/QP0TD+BpLtgt2PZuw9tz54o/D1/gGKbpMPW/vpfyMl/lcROt3H50w9gza2n5gspOc6aVedhoJxpbNy4EWuKlXcDZ3Fu4DLidUXA6dFtW00mZGW7cKn9KdF1JylMT/OOChBF9MNikMll7PijgD3LJRK62z4ci1zxN71wDiyA3VL+C6ILJr3g1aW0tJRNmzbRp08fUlNTz9ipzS4XDQ6XF2dOR3hs82MsLVgKwPbUL6h+W8rRMW3cRJc/Fp7yNez9cxEul5NXLd9Qv+RzAjWBbJ7VTt04UFO9GqNxBwAF+W+6VajPoO9UFAWczhbaeWcjDpeA6rjPv+pIARX5uaSOHINK0z4Zn2B30bynElWMv4cH4RhUGi1JA71LhU8Ei0vALggEqrxfe+15C7sNHYngcqHx80Ptl0zWtnJ6j4t3h33jB0lJzrGe5HOCS8DpEFBrT+0Vq+0egt/ASIRmJyGXHldRJYpnkgqnFSHXXEPINScnUDilyxSmdPEt9Lmw2khopwBuCwwhWOfbAzv84iTKCxpY+XkGlzw6kK4X3kjxLx8z97PvueXWvyaS+C/+93B2mqxOB3KVGkEQEZwi4yKDmBkZzK0ZR3gur4x6R/ucCBVPPsXR664jZ6Anl0mp0cLwV9bS+dFl7G5R7AU4WixNpGUZf1C/MA/kEDSjK4FTOhN4RQrPbH2GixZdxJGGI4iCyPLPDvHhrWvJ3Frmde5t27bx8ssvs2WLb7rp4rpmbvt+Dx+uy0MURUaOHMmv/hezWzaUF2XPETF4FEVFRfz+++8cPXrU5xjLD1Uw5b1NzN/lbq8pLuK3hy+iaf6FzP99Jh9+ci/T3tpA/aI8jIsLqF8gcUG0VT/uKHBYXv47Bw7cismU3W4fURR5a89bTPltCnsr93o2xg+BFk+SLWAILpN3suiqVavIyspi/vz5Psc/cOAAr7/+OqtXr/Zqy92xlfnPPkL+np2t+6o3/kzBvPcZsyOL/tsOc1emZIiZzfls3TaBjZsG43AYfd54dp37PtWJiShCJd2nk50UjsfgGRejMwRgC5A8Rw22jlfuoWFjMRh6Ipdr3crMx+WgZOc8y5q1XSkr+9nnGNnZ2bz00kt89913Pstd5XIl/fvNY3PltcxZ8yrJj3tWFjntduY/8zCrPnuf967xTqxsi6Z1xRj/yKf643Qclb4TgU31NtZ+m8nhzd6/E1+otjsYvP0w3TYfYm5L8nNbtI10C4LI4c1lFB6oQSaX02PUOLoOGEJ8agiNI7OYuHEEt66+Vep85a/wQC7MdldiOewufnxuJ3Pv2cjOJYUndX3HIFcrCLk4hbBreiA/zrj5OzwoZxJFFjs9DToGxgdR0WD12UepUnDuTT0xG21s+DGbSy6ejC15GMb1v7JtV/r/8xX/i386zkoDReZ0oFBpcNqlMje1WsF7qQk82iWaL0qr6bc1gwv35bGxrsnrWJfR6HPMfUfrKW/5UX683l1hk5h4O0plEIlHH8e8vZyauYfwHxaDYVQch42H+TX3V3Lqc7hsyWU0N9rJ3ysJGa79NsvrHJs2bcJms7Fq1Sqf1/DF5kL+PFTB6yuy2V1Uz5gxY7gkWSol7KbXolD4sWTJEtLT01srcI7H6yuyyChr5OFf3ZUtdWUlHOw6iM+briZ2YT9u/XELdUVHyZl0PUWDn0fTwjw68pJkhkzvwtSpOnJ6ppHZPRVXg+fkKQhOMrMeobpmFTt2elY3tEW1pZqvDn3F0aajXLv8Wo82a2MslhkHqIl/m4LrHiJn4MDW8zQ6XexqMNMlSbpvpdL3Cnbz5s2YzWY2b/b0PBxqaubHzz6k5PAhFr72HAA1has44HyEvVHfUGKTEkAXVEhJwlVVf2KxHMHhqKWi4g+fJcEvjnyRq3tczW/Tf0MVEUHS2jV027OboIvaJ/bqCGljJjDn8x/5csrX3N3/bjZc5l223hZaTRSDB/3BuLEZaLXHtFBkrfaJ02mmpERiN83MesTnGLt378Zut5Ofn99ucqSfthuHc0chOFsSjB3uChiZXI5KK+0/UahFrneH1mQa32HPfSuLyNxazrrvs6goPHFopdhqp9ouLTw+L/E2UNoib3E+Zb/ksPrjA2RsKvVo+y33NwA3OaJc7pXgbDbaMFY2A7DrFA2UjiCIos8y41NB8/4q6n/LxWm0ndbxpi2llDyySVpsHYfOfhoKmm2EGzQdVpgFRfox9spu5OyoJGtbBXc9eh8Wv1BWfvAGxgbvd+6/OHtxdhooLgdKtRqnXVoJKtVyFDIZd3aKZM+wNB7pHM3uBjMfF1d5HRv94gtEPv44XVeu8Ng/MTWSywcnML1PDO9f0a91f3zcNYwZvYcAH/HylOAU+kdI5b2vj3kdv0A1/c5JICBMy8z7+3v1Hz58ODKZjLFjx/q8rzHd3IRKKZFSHP3ZpFiKxvRmw2Dp/J07S9o0er3vyqSLBkjy9ePajJU0cCj6hv4YGi8gN/Ve7Cp/3hAkPgxrUD76AVKCp1+AmoGTE9FmuDlILAcOeIwvlysJDZWuPyrqAp/XABCmC+OcTpK26nPDn2vdbytqpObLQ9TOy8VW4L4HobkZURSZvjeXaXtzudmi4txzz+W2GcOxZm2mOLMOl7Nl5V9fxFXCAi5hKf379PI47x9VRjK6Svv8ekj5IU65xKgZTD2PlXzPPZ0iyW5JVIyKmo7B0BN//x5ER1/k04PSI7QHDw16iORgyWiSazTI9XpMdhN3rb2L6QunU2nuWHzOF9LC0rip102EaENO+di2S3GlUk+nhJtRKPzpmea7kmPIkCEEBATQu3fvdsMhCz7bwOgykbsbdPzQdzO8GAXvtghSKpVcdc9sLrl4GLe880GHl+Y/Iobw2/sQ/fgQlEG+Q0ExKUGt20ERJ85n6mfw47mkGO5MiGDd4PbDnILdhW5HBd21Cs4PlEjN2mJ279kkBiRyex/f5evHrmfUZcn0HB3LjW+05JrYm2HF47DhtdYKnlOFS/hrIR7B5qJufjbmnRVUvLLzxAf4gKnFs2veXu7VFqJSYHYJyGQydKqO8+lSBkeROjyajT9lY28QmHnfI2hsJt57/pV2Cen+xdmHs7KK5/mrZuHXczizZ8/mu8e3Mf2uvsT38HzJf1pcxUsF5ewZlnZS1T2NNdX4B4d40W23hbPOSvOhGlRhOnQ9Qv/yffiCSxCRy9qPqQNYLBZ0ulOjX9/z6262r5ISBgfU3k/zjY0IKsg8PIY77/T0xthLSqh4/nnUnToR+cgjPquSBMHukdB7MjBWlNOQXYZieTOI4D88HGfZBnT9+6EfPBhRFEnbcgiTzcLdG74kensWoc1KAkJq2Jl6B8qGLhLvytoXYOPr0qAzP4U+bsHCvGYrtx8uwl8uZ16frmgVckRRoDTvOzA5iO17o8ezbWrKpLziV6KjZmIwpHlU8Zg2bca0bi0h116LupO3Ps+KIyt4YMMDAAyLHsZnkz47pefxVyCKIrZc46lVHLWDtWvXsnHjRmQWLWENgwGYwF10j2oROXymAZx2eL0r2Brd+9rAbDazbds24uLiTloTx2FzoVTJJVmFv4hjVTyiIFL59h6c1RYUMXqi7/JeKJwW9nwDiyV+HobdAee+eMpD1DSY2Zl9lMmDTy+nShREqj9Ox17chF+/CEIua7/KrT2Yd1fSsKIQ/6ExBEzwFLt8KLuYHQ1mvk9KoKrJxoBOHX+3HDYXP7+yG5kMLnlkIPPmL6R6yVeEnH8N11936Slf27/4z+PfKp4zAIXLgUqr9fCgHI9LokJ4o7CCVwrKeaN7x4Jz2379ka0LfgDg9qdepeaDDzFMnEDghRe2TmaiKLB0x89sytnMpTXn0mvWGHRpZ95IWX5kGWuOrmFO3zl0DfJd9tiRcSKKIll1WSQEJHjwv1SN7MoXQhZ1DQ7iSu7iwsMHURaOwqL1ptlWx8W1ytu3h1M1TqxmE989chd2i4XOnfsz9Y4HUccZaJv0K5PJ+LlvEukZa8ktPUpSeQMGh5W94TP4Nu0drtrd4onpNhl2fCpNlknneJwnyU/LyoGeL26ZTE5csmeY6Rgysx6hqekQxcVfeZHnld5zD4LZTP28H32Wbw6KGkTP0J4cqj3EA4MeOKXn8VdxJuQbjmHHDikBV9RZSdm9gNDaQ6ztMYimEUMYNKqFK0SuAF2Q9Mz13tTpGzZsYGcLQ/GcOXNaOXs6wvHU838FJpOJ3FyJMl+cbEAwa1EEasjLc4cykpK8K1dOGrH9EWRK5KKTyzZH8u0EF5p2BPgsWXXY8o0YRsV66HPJRBeiL6e3KML+eVJ4sc/l7TIPy+Qywm/rg2hzIW9HJPBE0A+M9Mk2LIoiW+pN9FKr2VFYx0X9T1wRqdIoOHd2Gr+8vJtNP+dyzdUX8fyhdBzL57Gvf2/69T69hP5/8b+Ds9NAEZ2oNRqJuho8WBWPIUSl5MmuMTyUU0KiTs2chAifL3XBbqeywD0xVX/wIeZ16zCtW0fg9OmgUuF0NrFz1wX4a45gilVxY9BW9gRt8xzH5fLwvgii4LN09Ph+beESXDyx5QmcgpNVRat8MqSeCJ+kf8JH6RJ19a4rd6FVahFFEcVdd/BD+l5WDBlFn+depuK7HtQ5zPg7Tky21haiIFL/Sw7WrDpCr01rrdJwNdqR+6vcq2HBBc114B/eet+uFkG36sYi1IWfweF6GPsYqN0u/oWV9bzfHMtlYiwGhxT/D67KxC7KuOTiFobf2P7waPEpPxtfMPj3oKnJN2W5rm9fzFu2oO3Vy2d7iDaEH6f+6LPN1WCj7tdc5Co5IZd3R3aqWjttIIoiP1bUIQNmRflWsf4rCO4ZTMH+AvqkHyKutJAj0bE8N/tuACoS+0qd5Aq4eQNUZ0tJzschMtI96bUXfgTflXLtocHWgE6pa191uw369evXYfsx4+V00bivhNIfI8gPjGHn2M6U1zQSZVC1khoeg+gQqP3uMLhETJtKPYQbbXYHGl/e3NxV8EdLyKlkF0xtX8xRJpchO03jpCMsrjJSWGTk2m5xXNQn9qS/Y6Ex/oyalcK677KISwnmrscf4p075/DHW6+Q/OFH+OtPno7gTMHhMCIIx3J0ZJ7/ttyX++482933LWunTYZSeWrcQGczzjoDxeV0oRRdqDVaHC1Jsr48KADXxIaxoqaRFwrKSfPXMS7U02VV8vk8cr5fRVTDIQLunk2X/oPQH87BvG6d9EVuCW1YLMVYLEcAmBnsQB14DupY95d0x8Kf2fzjN/iHhnHLR1+z/MhyHtzwIAB7rtqDWqFGFEUWvvYcBXt30ffcKT7FyRRyBaMjh7O2bAPnd54sab3krpJKIAOiEQSRFXMPUbCvmkk3pZHsYyVUZnZXRTgEB1q0iHY78YekDPtzd2wiOTqIzJGxbPwph7junm5cu6WZJe++RkV+Lpc/9xrB0Z4rKUdlM80ticDVH6cT98ooqlYe4aW1OViAt58dj59aAV9PhaNbofNouHYxfgGBXP7c69SVlZASo4BvWxg6c1bAHW42yu1GKTnv94mzCK9SIBcFCvpU8/hPIk2FN0KLJ+PQulWs+ORdkocMZ/p9jwHQsOIITeuK8esfQcilkhfF1dhI+ZNP4TIaiXv/PRTHuS27d3+JxMQ5bZJP3Yj/7FNc9fUowzrmW/EF895KbDlSIq5pRzmGEe2vSCsqFpFx+F406khGjNiETOZpwK6ubeS+LMkg29/YzKvdWjyCHUR3tyz4ge2//kh8Wm8ufeqlDq+1KqyKPxP+ZE20yG/Pf81SMQoqPTVyRFHkcOGLVFQuJImH6dTpZo/2AQMG0LVrV/R6PSqVb/6ZIouNaXtzqbI7WTEwhT6G9ievTSWbuH2NNGkvu3AZ8QZvL6gtLw8UCjQteVknC2OznddWZJMY6sfsUV28JmOhuZnyJ5/CUVpK3IcfoAwNxfi7RPPftaGMm4bEsOTx27GZmjhn9h30nnie+2CFDFWUHkepCU1XT+PfYneg8VEijSESZAqp5D66zyndy6mgtrYWpVJJYKDndZmdLh5cm8347uHM7hN3ygZw6vBoSrLqWfdDFpc+Nojz736Qda89zrsvvsHjL/mWEvi7YDYXYLUWo9FESSrq0OZ34vlv++3tt4miC4ezAblMSVDQEBSK9svt/8VZaKCYmqXKArVGjaslxKNQtcMU6nSR3Wyhp7+OQYHeq7q1OzU0db8aJTYun9iZufOX0tTUxCUL5pPW203A5e+fSpfO99Jcu48Y/bmc292TiCp/l0T6ZKqVaK/XF69vbTPajET4ReB02CnctweA/SuWMuGG2ygu/pac3GeJjJxGz7R3aFy1ilvvWsutQMrup6WkvF1zpYGeqKap3kXBPqmCYeXnGV4GSuO+o4z8QUCnjiB2cghO82FQD0Gu0RDz0osc/uQ3SiPGkHP/Rqbf3ECv2JshZBiIC1tXFkcPHaBwn6Sr8ucHb3HFi56aQqoIHdq0UKwZtYS1kFCtzapiIQ60OLjz+518cd1gKJXulcKNrcdGdkkisksSjoZS5P4RyExVOIbdycKKOrrqNPQP1PNiSixfl9ZwYYqNntOewKCPpfKJJ2gozELThg/lmIhb7g635pJ5t6Qp07y3qtVAaVq9hqYWdd2KF14g9rXXPO5HJpOh08XhCzKF4rSMEwBttxCaNpYiWpz49eo43FFZJXGs2OyViKLTy0CJ0qhQyMAlQp+Ak1uRZm+VnntxxoET9ITb+96OXqWnf0R/OiUM4VWni4mhAQwNchvhLlczFZULAcjLf9XLQAEICgpq3W6yOlAr5R5hkO1GM1UtlThzi6v5oId3Xs8xHKp1e7Wy6rK8DJTmvfsouuIKAKJfeJ6gizsufW6L77cXMW+HVIYfpFNz6SDPsU0bN9G4VPpMSu9/gE5ff0XojTfhLK9AP3IkV3dT8fN8qVrl4LqVHgaKTC4j4rY+uMwOlIGepIw2uxOt2ofxFt0H7mohxgtu/5kcD1EUKc/NIigqBr+Ajj2hhYWFfPONJFFw+eWX062bOwxqdgk0yUSuigv3ZJR2CWysb+KQyYLFJRCqUjIwUM+AAD+PfjKZjLFXdmPBi42s/DyDix4cwIEJl9Gweh7zflrEFbOmn/Q9/RU4nWaamwsID5/4t59LEOwYjbtwCVb0fl3w8zs1I/lswdlnoJil8j+tzu1BUfkI8bhEkbsyj1LncPFr3yT8fcSLNUEaeotf0Ve/CMfXepqc0kt345YtHgaKTCajs/858O2TIP4CvVfDhe6EyNFXXc/OP36h17hJANzU8yYabY0MiR5CuC685Ro1TJx9O/m7dzDqiusQRRd5+a+hNsWg3TaA6h2HcBxxT7aOkmIUHirMIgFhWnqNjaPwQDWTbjiObVVwse+zqzhUF4AGHbpXmnn5ie94bbzkjl+eZOHHcYU83DSVeIcS+8L30LhsULDeY5i4Hj2JT+tNaVYGk269y+uZyRRyfohW8XZGIzfllPNESjDDpiSR+k0hQ+SZUAzlVZ2JvvQbyFsDwz2F9kqaSrjizyswhWn44OJf2SfvzfOZ0mSxpH8yAwP1PBScTkbGPewBBgxYIFVePfYYiCrsJU2oYv0ZPOMS1n07l9SRY1vHDjy/M6aNpRjGuQ0O/dAhqBIScBw9Stjs/z9hM3WMP7FPDztxRyCx0204HPWEhoxGJvMOZ/Qy+LF9qKQ0HH+SBHOjr7qB3Yt/pd95J54cwnRh3D/w/ta/9UoFF0R6etaUSj1dutxHRcUiuqU83eF4OwvruPRTKQS64p7RdIuSKtKmhgey1RiCQxR5rVvHeWFXdL+CJnsTXQK7tFaDtUVbugB7cUmHYx2PgYnuhPohXbwrqPwGDkCTkoItJ4eI+6Xnoh8ymC6LFwEgCgJjrroBm6WZITMv8zpeppR7GScAFrsTra4dA/MUDJNj2LXoVzbN+xqAWz/9Dn1Q+0mtjY1uRt3q6moPA0Utl4EMchrMnBceiEsU+aKkmneKKqlzuAhRKfBXKKi2O7AIIil+Wl5MjmVUiJuxV61Vcu7snvzy2m62/p7HDTfO4vnMAxT98TUZfdNI637yMgKnA1EUqalZQ2Skt3bS3wG5XE1IiKShZTbnU12zBoVcS1DQYOQnEKY9m3AWGiiSB0Wr0+F0SAaKLw/KJ8XV/FnTwDe9OtNJ55tefvr9A1B8ejsIoHKaGTVqFBaLhUmTJnl3FkW3u+8413pcak/iUt2U1knBSXw00VPCHKD3hPPoPUFabTkc9QiChcDSGWibErE11RM4ZRZCkxG/fv3Rdu8OXV6ChOGQMASUGmTA6FkpjJ6V4jU2djNOlwmQQhgxdZX8LnuU14BmRzMv7n4JMVLk5sjn+DPzIyzBl6NxVkPXCR7DaPX+JwwJfLPtCACfby7kiak96NQ1hCfPS2DlSin8UlZWRvSA86Hb+V7HZtdlU2+rB5mMH/J/Z3D3vq1tfi3MpQ6HO7zgsNchk8lYW9DI7Hl70QKrE2NJvnU4yUM8RRv1/SPR9/f0KqliYkg6rqT8n4bAwL4MHLCgwz4na5gcQ9LAIafF1toROifOoXPinBP2O1BibN3ell/TaqDolQreTU1o5yhPBGoCeWhQ+9Tt/uPGEvPGG8hUKgyTPA0Y4+J8zDsrCL40xaf3amiXUDKfOw+VQobSB1uyMiyMLov+aPfcMrmcgdNOnQPHancQGHjmJq/mRnclldNu77BvanQStfreaGRqhg3yNJzlIkTJFLxUXMXOZitGh5M9jc1cHRPKTXHhJPtpkMlkOAWRNZVGXttbxCX1OTzdI57b2oirhicYGHFRMpvm5xCbEswdTz7Kh3fdzoLXX+aRDz9Ep+1Y5uOvoK5uE6Gho5B1IBnxd0Gv74pe3xWXq5m6+i2IoguDfw+02r+PZfy/BWedgdLcEuLR6dpW8Xh6RxocTt4orODmuHAmhXm7Prdu3UppaSmaQcPZOexVbq9cSszI25iQ2L6qbIPWRu34aYQbBmHoe2e7/U4WKlUwKSnP0CjPQlYtR6ZQoB+chGFUmwQ5tR76eK/QfEIbwKjbHsX07S98Lozi694pLO8jJQ7qlDqGRA9he/l2xskGEnR9N/QpI6mp7YrJnEu8YEGhcK/sRFHEWVWFMjzcZ4nxfeek8OG6PO4cn9y6b+DAAVgtFsxVIhEduDtHx43m+rTrcYku7u5/Nyq5imQ/LbFaVashGRtzBQq5Fq02tnWVsuyQxNtgBb4vquVeZ/tVFH8FgsWJNafebYT6isd31NYeRPHU+p8ElD60mE4VVoeLBbuLSQr3Z3jS6YWzjseswQlUNFiJDtJx7fDEMzLm8ZDJZARO9aZsF5odmLZIeVh1P2Th94rbQDmWKGvLM2LaWoamSyCGUb7De38HzI1GMgU/SpvaZ7ruEqYnPuTkPtfhl1xBYHgEUUkpBEZ0LFZp2VNG0hEXMv8AmvdXeUh0LNlXxuYL+rOyvonbDxchAN/37sLE43L2dhXWoRdEVpzXm9s3ZPNcehFxWjXT2siL9BobS2l2PWu/zeTSxwcx7vb72fbOM7z7yjs88szDJ3VfpwqTKRu1OhyV6j8rWKhQ+BHWwhHV1JSByZSJUhlAYGD//4jh9E/AWceDsmXbfra/8wTD7nsR/+ZQtv6ax60fjPXo80VJNU/nlbJ3WBoRxwnGGY1G3nnnnda/PxlzAVENtVx35CDh4eFEWhuxNDUy6eY7aW4UUeuU6AM17Np9MY2NUpx4wvh8XC6BshwjYdE6rH8uRBEainLCRFbXNjIo0A+lTIZCJvOQf3cabSCKKIPbT6xqbm5GpVK1m2jocgq4nO1rhPyxv5S7f9ovXWf3CL64bhAgGR0OwdFaEWG1VbBly0hARCZTM36cu4y28uVXqGuJV59IHbUt9q4sYttvUkXUtLv6kHAGuWIyyxt57Kd9pFeaEACDVsnBZ849Y+P/p+GobqZxZRGapKC/Vd/peHywNpc3VuYAsOCWYQzu3D5pnCAIOBwONJr2V8I2lw21XN1uouWpVPGcDkRRxPhHPuYd5YRc2g2/fhFefSrf34ejVCLva1tl80/AhpxqxqScuET7VFF48WVYDx1A5hdCyub1yP2k98vm3Bq+2lLIR1f1R6NUUGVzUOd00l3vpjKw2F2sy66ib3wQMUHSflEUuXBNBlmCkz0Terd6PwGsZgcLXtyFPkjNBff355OPv8a66TcSZ93JRTPP7G/W6WzCaNxFWNj4MzrumYLDYaShYS8gIzCwPyrVqVVN/n/jXx6Uv4hmi+RB8dPrcDa4fFbwLK4yMjYkwMs4yTfm8+bON4kNjsNRb0fRR9LjGVNTjNFoxGg0UpZ7ALnTTl2piKlJIq264L5+hIQMx9i4H6dMcldv/SWPA+tKiC7fSmq2xKGy+ZY7eLKvtOKXAwLwZc9EJocH4agwU/n+PnCJBF3QFf/BkVC4AcJTIUCakHJzc/nhB2msO++8k9BQzwne1uxg/gu7aKqzMuLiJPpO9HaXD+sSSu/YQA6UNvDI+Z4cIzJBxup1q/Hz82PQoF6o1aHY7TWEhoykqSkDg0HKa7EcbL+8WRRF1n2XRebWckZekkyfCe5cAn2buLvG3oDlQCnaXr28JqPyvGx+e+VZZDIZN73/OWqtJ6+L1SVw1YECNhtNfNurM5PCAkmNDuDb24Yz8tV1NFgcdAnTM3/XUaoabcwe3QXtccyXFXk5/PrSUzjsNm799DuvclCHw8FPP/1Efn4+1157bStD72mheCf8cDHIlXD3AdCcehli07piLAdrsBysQdstuF0G1jONCIP7PCH69sMPgiDw5ZdfUlJSQv/+/Zk+3Tu3ZX7WfF7YIQk/7r96P4rjVKrzqkxc9PFWGiwOVt47upUtuSNs27aNFStWEBcXx0033XTC/jKZjOALkgi+oH3OE8PoWIyLCnwaLyeLqiMFVBcV0m34aJTtLCbaosBYwFNbnyJGH8NLo15CKfd+df+da017saQ9JTbXtRonAFqVnLsmJPPnwQq0KgVqpQy5TEY5TYiiiAwZIiLn9Ij0EI+UyWS8PTyJEX/sY1FVPbOi3e8qrV7FpJvS+P2NvexcVMBtt1/Hc9kZZP88l9w+PUju0nH+0cnCZqvC2LCbiHDvUPI/BSpVEGFh4xFFgYaGvTicDWi1sRj8zw6OmLPOQLG0GCh6Px31difK4/JPml0COxvMvJzi7bp9beuHbK3eBIFQ1/1NXOoI1qR0ZmFlA3JtJSFBgchri2msqiSx3xgObZTO1VBtIWXYvdxeez7pJhuzc0sYY5HctDZNUOv4Nbo2PCgt/26qNzE5PAhXo10qwwDsRU1g+Ro2SHTzPFQIfiGUlblLhCsqKrwMlMZaK00tUu67lhR6GShGh5OLMgvJ7enPF7N6khzuOQGkp6e3atfYbDZGjlzJ4ZoM7sssQLtpLaJpD69fO5m8a4eSlBhO50u8yc0cNheZW6Vwy+afcz0MlG5DogiJ1qNV2Ci7cCp1DQ34DRtKp6++8hgje+tGrE1S0l5pZgad+3kKN2Y3W9lslFa41x4spHxcXwAMWhV/3j2KknoLfmo5U9/fglZh4XDBQt699gbUavfzytm5FatZGqPowH66DRvpcY7y8nLy8yVvz48//shjjz3mda8njUO/gbUlH6BkF3Qdd8pDaFOCqf9+Lvbc5filPUrI1Ve2ttkKGzBtLUM/JApt0pl1Y186KJ4eMQFEBGgI9ZMICX25o+12OyUlUjLq3r17fRooW8rcIpgCAgo8DZTtBbU0WKTE7592FvPUtB4nvL4DLVILx859JuDXJwK/PqdvnNgtzfz41IM4bTZWz/2Qu7//7YTHLMhZQHp1OunV6VyccjGDowd79XG4RFQ+WHVry0ws/eAAdquTq54fhrYDQ7ItRFGkxuEkTKUk/uOPMG3YSPDlszz6HEsY7hZlwOESMGi9x64rN5O1qYzkQZEe51baBLoE6thcb/IwUACiugQy5IIubPstn9iUYG554jE+v38O37/8Io9/+D5qX9VMp4DGpkM4HQ1ERrSvB/ZPgkwmJyhIes9ZLKVU16xBLlO1lCr/fbk5/2mcdQaK1SJN0P56P2oc9SiOyz+ptDkQgK5+nh+6yebEtbQMBgMyCGlyUR0K9y7YT06lCejJkUemIIq3AJIiakhMGfpADV36hVNjd5Juksh/5pbU8MRlPYnqEkh014H8PGcxk2KzuavqaVJdw+l//U8srWlALZdxfawU19ckBxF8UTKiKKIfGAUrTe6LEyRjZ/DgwdhsNkJDQ0lLO65KBwiL82f4RUmY6qwMnt7Fq/2wyUpus3SNT+WWMiU8yKM9OtodOkhKSkKlCuTbukBCy0wsqHkCgGHz1mAK2wvdYHGXe0g87hxqrZJBUzuTta2cMVd4U22HJxhw1tgQTNL9WQ97h4h6jT+PkszD6IOCSOjlzfvQ01/HVdGh7Ggw8XlPT89GTJCOmCAdNSYbwX4qLk/+nAGRB9i0+RMPJthe4ydRln0YnSGApEHeyaIxMTH06tWLoqIirrzySq/2U8KA66B4OxhiIHHkCbv7gq53GPb85SC6qHzxBQ8DxbgoH0e5GcvBmr8Ukqg32wnUqZAfNwn2jA2kunoV63dJCr+jR+1BpQry6KPVarngggsoKipizJgxPse/o98dqBVqJiZMROWjkmFanxh2H6lDLpfx8PknR9M+fvx41q9ff0IitjMJURBoWr0aVVQUujbVfMcgk8tRqtQ4bTb0ISenozSp0yR+y/0NuUxOz7CePvvYXQJqH4R++XurWxcmeXuq6Dn6OE6dsv0SE23fKyCmb+vu+7OLmVdeR7BSQeao/vj196T9X5dVxTurc7hsUAJXDEnw8kKCZOT88fY+mhvtbPwpR5KaQJLk2F9spE9iMEetvhN0+01MoDTbyOqvD3PZ44MZduPd7PvkJd5740MeeOyedp5UxxBFkdradWh18a35af9t0Oli0eliEQQb9fU7EUQ7/vpu7dId/DfjrDNQbFbph2rw1+Gw16I6LsSjaHn3Oo9zlyrlMrpUx3Dvy+nIgZVfRzO5V3feKTvcYqBIOBaOUChk9Brr/sKEqZW80S2e3Q1mHuochUarpOfoWMwmE9XJPflWP4zZwjwuqF4LfhqP7PZj4+oHRbl3jH8CIlIlVtQWNVWdTue7gqjNGP3Oab8KYnCgnlviwjlqtfOGjzLO2NhYHnnkEeRyOWq1lIsyIzqBtbuPtPaZHq9iXouIrVbpO8wweGpnBk9tPySiDAuj09dfYcvPJ/BC72qH0Lh4rnq5fbZMhUx2QnmCMH8NGx4aR9bhBTQZvduDo2KY9exr3g3HrlGp5KKLLurwHCeNiO5w8/q/NIRMLifslluo/eILIh991KNN2y0ER7kZecCpVfK0xeebCnhhqWQs5r80GcVxRkp9/fbW7ebmAgIDvTVs+vbtS9++fds9R0pwCm+MeaPd9kCdindmnZqhkZycTHJy8ok7nkEYf/mFiqekUur4Tz/B/ziDTKXRcvWr71JXVkqnnidHrNY/sj87r+xY4M/lEr0+F5A8k0czapHJZHQbEuV94KI7oOIg7PzUQyNpV4NEeljvdPk83+srsjlc3kh6yUGuGNL+e0WjV9Hc6DZCmu1OVmZUcl7PKJbmFLerWCuTy5h4XSrzX9jJqi8zmH7PUDL2TsaxcylL/+zHlPN9G7rtwelsoqZmHaGhY/7xuRwnA7lcQ2iotOBoMmVRXbMGpcJAUNDA/5mk2rPWQPHTaXHZXShU3qRWOrmMDJOVsSHuJB+tSsFNnzzHlnu7UC+GYfi1ieShWt67vB/3TUqhc6gnkZs1px5njQX9oChkLWGkIaVO0owQmeR+7GXl5bj0UijlS9lk1CNETipXXe0H/a8+9QfQAZRyGc8md6yhodV6Gh2jQwyMvvxJ2OwPgfE80v8aJlXtJcGQQLjf6Sfr+Q0ahN+gQad9/MkgQKuiX6+3qK1dR3Dw8BMf8A9H+F13En6Xd4VY4HmJBEyIR3YChdmOsOtIXeu2IIoo8JwIExJuwulsJCCwn6dx0lgO2gCpoux/BEePfkFu3ksEBPRl0MBfvdplarXP7bYICIsgIOz0w0S+4BJ9GyiB4ToufnigjyNaENNPMlCOw1vdE1hQUcdVMb6T1Wf2i+VweSODE9v3AslkMi58oD81JSZikiSjYGNONdP7xCCXyzhstjLYBwnmMegMas65MY0/3t7H7mVHuOPum3nurkz2ff8hvXp1JyGu4+qjYzCb87FYioiMnPa3JVj/J2Hw747BvzsORyO1tesREQkK7P8fr0z6qzjrDBS71YZDpkQul+OwC14eFLVczpgQA79V1nF7vCczYligHzUhvbFbnNAkxcIVchldwz2TGp0NNmq+OgQiNK4pIubJYdSWmlj5RQYAmVvKuOKZoTiNNgw7rCSFdaLMWk3/KSPpFz8Ks82JXiN9NA22BtR2ORq9Hy4HFB+uIzopEJ3B+8UniiJHjx7FYDAQ0sZ17LBaObhuFVFdk4hJkdhUzQ02FEq5Vzza1mzGbKwnRNUMS+6FsCSY8pakpdJyjiyzlUSdBt2xpDe1XvLoIKlOJBgSfLro24PL1Ywg2L3CAmcCoijgcllQKn2/BFWqAKKiZvhss7gENHIp6c/n2IIIgviXdHJOBhaLBa1W+5derH/FOAF4+LzuhOjVTEz1THY8Bq02mh49XvfcmbkE5reEmu7cC6Htk201Ol18VlxNb4POZ2l/KwRBKrfuoMpHFETkPq6xdQiLBZmP5+kURJRtJnhRFH0+84pKieOksXG/z/EDZ8xAFRmJMjwczWkKDNqLJaZZdfyJE4GPQVIyP43vyLT3YMzDEOC5OBkUqPfJoH0Ms0d3YfZo71Dx8dDqVcR1kyZKm9OFVqVALpeR3tRMttnKI519eHXaIDYlmEFTO7NrSSGxyUHc+NjjfPvwnXzx4gs89f47KE5AF1Bfvx2FQv+PrdQ5k1CpAryTajVRrQUM/2046wwUh82Kq2XydPrwoJhdLmrtLoqtdqyCiE7h/sHLFXLOnxNOdZGTHsO8s6htRxowLilA0zkQNAqwutAkSi9brb8KtU6J3eIksbeUV2LaUorzkJGxJBF2wwWkKwUGvLAagIVzRrCqYi7fHv4WuQuuWdGJtAkvkr9XUg8+Fstti/T0dBYuXAjAjTfeSHy8FObY+ss8di/+DTlyrn74bRz+Ifz2llTyPP2evsR3D2l9Nl/ffzumulq6xvlzgWEzFG2GPldIZG/Aq4UVvFNUCUDp2D4ojnshplenc9WyqwD4ZOInjIjtOM5rt9ewfcdkHI5akpIepVPCiSstAFjzHGx6E0Y/BOMfb91tqq9j9ecfotX7M3H2HPalX0Fj4z5iYi4jtXvHBHJtsaqmgasPSmKDh0b0JOw4kTaXyU7VB/txGW2EzOqGX99TXw3/WW3k+kNH0MplZI/qhcYHZ8yWLVtYtWoVAE8//fQJjZSaTz+jccVyop58Er9Tzbuw1ENVFsQPbjVIj6FLuD8vX+idT9Ehqg63ubCcDg2U94oq+eCopNG0bEAy/QN8TIxl++GLc8Blh7v2Q4hnmNBhc/Hra7upLTUz5opu3rkWQMPixZQ9KBG4ddu3F3mLsvfjOSV8UVrDkEA9f/RPprT0J7KyH0epNDB61D6P5961y/0UFL5PTMwlXuNDSzh22MmxAPuCraiR6o8l7auAcxMJGOcZrnQIDr48+CVqhZpr065tFRUV2nhQTCYTixcvRqlUMnPmTJTKDl71MhliQCyVjTYiDJrWHCNRFMElGeBNdVZcDoGgv8ids2BXMZcNSsAhiDyeU0JXnYZzQk8cbhlwfiKlOfWs/DKDWU8Mpt9Vc8j8+nU+ePcz7r7fW5cMQBCcVNesIjCwH1pNx0bQ/xraJtVarWVtkmoH/1fp//xvBKpOAU67DaGlRM/l8PSgiKLI3ZlHyTBbmNe7q9tD0ILa2o1kH51KnewCLPZ0r7Gb1hXjKDFh2lRK1L0DiLirH6FXS5UG+kANl90cw3m1nxG/4g0Eux1tstv9por1J7uyqfXv3Ufq2F0padoILXOFvZ1ksmOw2Ww+t/WBQQAMCjsf64IyXF+6dUrK89wxZ4fNhrleYmHNLzGBTC6Vvka6re8Ci3tcX1WNRY1Frdt7q/Z2eL0g/XgcDsnoKi2dd8L+rdj+ifTvRs88kUNrV5K/ewcZG9aQvvaP1lVuWdl8j34VhQ2s/yGLqqJGfGGL0Z1XlNXCPtwWzqpmXEbpWTSsKvJqPxksrZaevVUQqbb7Jt86cuTISY/namyk+u23sR3OpOjyK07tYkQR5k6Ar86DV0+uZLq2diMHD96BsWFP6z5nfT3OGklTiiG3wugH4cLPETpPQGhu9rxel0BtmQlREEn2c780o9qr0CjcIBknAJmLvZobqi3Ulkp5ExvmZfscwrzVrSLuqnOHrZbXSJ/Fjpa8i+pqiT3Y6WzieISGjmHQwF+IjTlJEsRThOgU3Ns27/yP1UWr+WD/B7y15y1e3P5i636X4DZQ0tPTyc7OJiMjg23btnmNcTzeWJnN0JfX0OWxZa376n7KpvSJLZR/doAfntrOD09vZ9fSQo/j9i77g3euvIDNP313wnPYnQI6tRK5XMath4+Q3mTh7e7xHl6r9iCXyzjnhjREQWT114eZfO5o6DMR+85lrF7nfX8WSwnV1csJDxt/1hknx0OrjSE8bALBwUMxNuymumYNFsvR//RlnRTOOg+K025HUEgvQIfdhdbgfhkurm5gSXUDX/RMZKAP12bbD9Vszmu1UI/Br18E1ux6lGE6FAa1l56G9c/F2A+mYweaVq0icMoUYl8cCXJp1XXpwHhqTHYiDBquHJLAkJonmHfgOxILVYy5axKd+/ejML2auG6+Y74DBw5Eq9USHBxMQoI7aW3A1JnEdOuBfK0F5xFp4h02sytqnZK0UW4VXr+AQC54+EmqjxTS77yp4EP347mkWJL9NIwPCfD5Yjm/8/nUWmrxV/szJnYM36R/ymiXis6pM0HnHQ81GHqRkvwUdnsNiYm3+7wvnxj/BGx9D8Y/6bG7c7+B7Fj4M067jZRB42iyvYLRuJsunT11gdZ9l0VdmZmMTWU+vVG3xkdQ73DR26BjZLC3m12dGIhhXDyCyUFgOwm/giDSaHUQ5Oc7D+H2hAjKbQ4GB+qJ0fielM855xz8/f1JTU09ofdEbjBgOPdcmlasIOKB+zvs6wVRgGbJUMTW0HHfFmRlP4XVWkxV9Z9MGJ+PrbCQwgsvQrRYiHr+OYIvuQTGP4GzupqCCRNwVdcQ/fLLBM28AIDlnxzkyEG3R3BokJ4wlRJ9ey77vldCeTroQmCYN2V+aKyeQVM7U1dm9i3nAITddiuiy4l+2HBUsW4Py8spcXxbVstt8VLeVOcu9yCKAuERfx+Zn7O+HoVe75Wnou0a1CqkqU3x/s10CeyCUq7EKTgZGz+2dX/bEE9SUhKbNm3CarXSq1evE15LerH3Z27NlAw4V0EDrhajqfKIp0G/a/FvuJxOdvw+n5Gz3DlxTqONhiX5KMP9CJjUCZlMxu/7SrigXyyHTBaWVjfQQ6/1+Z5tD/pADROv78Hi99LZt+oodz0whxfvyGLbF++S1iOFqIgQGhv3Y3fUodXE/L/p6vy3QC5XExoyksamQ9TXb0enOznZiP8kzjom2Rcffx5H+RGe+fILfn55F2EJBsZdKYVrJu3KJlSt5Mc+vl3RgmCnuORbNJpIoiKnebSJokjRwf3oDAFEdu5K0cH9FOzZSb/zpxMUKVnwlkMZFM+ejWC1krxhPYqAgL+dGbMtXA02mtOr0fYIRRWma7knF4vefIn83Ts47/Z7SRsz4QSjnDwuXvMSu80adE3Lyczd6VEh8J/Guh+yOLypDKVGwS3vnlo1wMnixq93sSarigGdgvn1tr+WhOtwOVDKlX/v96T8ABRthT6zQBd0wu5Z2U9TWvo9/v496J84H+PiPdR+/CSiqRKSB/Pd009QqJTxelMFTS1Ci5rk5FbRvO+e3EJjteSF8mUk/i+jcfkKSu+5B4DkTRtRhp9aQnmjvRE5cvzV7vy3olozggidw049ITmvqonvthUxuVc0Q7pISbHNB6ux7K/GMCGBojIzDquL7sOjPcrM9y1fzOafvmXgtAsZdtHlrfuNywowbSwFIPS6NLTdgll5uJJz06IQRZF3iip5rbCCGRFBXBcbRope68Ga3RG2/Z7PvlVHufCB/jTRyC9P341fNx2X33wdgUH90KjPjOTC/xrs9jrqjTsw+PfAz+/UxSVPBmd6/j7rDJTnH3oSp7GaZz/7hHnP7iA+NZhRl6ZQaXPQZ2sGH/XoxIXHKbGWWe3UOZz0NEgehcpGK6IIOU07mbNmDkGaID6Ke5qV779Doz6Q0bPv4PCnb2O3SC7t++cv8XktDkcje/ZehtmcQ+9eHxMe3n6J8N+Fxuoq5t5xQ+vf7V3r6aDTup3YkFaHFRvG/KMMFFEUsTQ50BlUf9uk3+vpFTTZpNDNkVe8dV9OFtvLtzN7pTTBr71k7V+qjjrTcLmsKBRaKt7bi7NMCo84jrzMT6nDsYlONnftRakhhLV71iL3NxB+153IdTpstirWLb8O45FUeoxIoPfAu//frllwCYiAooNE2r8bla+/Tt0XXwLQ6Yfv8Rsw4C+PWWuysTW/FsNxMhYymax1IZQQ4keX8FNnKj5VWPOM1HwuVQZFPzmUxTlVjEkJJ1jv9hb9UFbLg9nFraSUQwL1zEmI6DhJGik0uPDNfZiMVqbc48+WHevIn7cKzYiLmNPmXfYvJIiiQF3dJmRyNcFBQ//WRc6/VPd/EYLDDkrpR+JyuFqFAgtbcivS/D1p06vtDkbtzMLsErg4Mpg7QoKZ/v4W7C6B4UNXAmC0GalzGKkPCOHrS+/kU6uKcWMvoPe2VcS0JPsJgpPikq9RKvTExMxCJpNhMmdjNks6JlnZT1OsHkG1w8mk0ACfX6Kjtc2sy65icq9owg3e7IFOwcnKIyvpFNCJtDDfWdv19TtxucyEho5FJpNhCAtnwNSZFOzdxflz7qWy0cq+o0bGdgv3SbxkdrlYXdvI0EB/ItsJSxzDyJAQ1tSZ6ClvhvtzPNpEUWTupgLKjFbun5Tik4GyLQRRYN3RdUTpo3zfW1Ml/HwtL9iLma+Tc9uAxzDrx3JhZDBd/byTwmQyGX4d8IKYXS5+qainb4AffQx+1Jnt/La3hFHJ4a3quifCe5f3Y0VGBTeN8qx0EEURU70NfZA7IdFur0GpDEAu976mHeU7WrcPVB9gQqfjvFz2ZshfCwlDQe+9ehRFEVtBAwq9ClXUmS33PZZwl6ellZRP1j0PmygxnQ4+cpjSoAiMzRZSn3/efcmOOlSGbMJ7ZdPsSgZOz0CxNZsRXC50hpN7GZrqrfz8ym6aG+xMmdObxF7ez8tstFFd3ER8jxCfRowgCJjNZgwG7+9Bpc2BTiEnQKmg3rgLszmX6KiLvNg+Q2+8EdHhQJeWdtLGSXtVRa1j+muY1iem3XaQtHrOhIEiiiKVRxrxD9Lg70MbTJsUROxLI0Em/dYMWqWHcQJwZUwo54YFUutwkt7UzLyyWq45WMjksEBeTIklWuP796lQyJl0UxrzX9jJ7uV7mX713by6x4Zz00I2DejLqGHeHDxnK0ymHJqbCwgJGYlS+fcbpmcaZ52BIjrsyJTHclDcSbLHiNlUx70AzC4Bs0uy8dObmimX67C3/O1vG0+f8Fp6hfVi5MCZ1Cji+NwljZ1nCKVbdCe0qVJZb2XVEvLyXgakxNCuXe8nMKAf8XHX0WwpRJv4IuP35iIAE0IC+KGPd/neTd/uIqfSxNOLMnyuyH/I/IE3dktEV9+d/x19I/p6tDc1ZbB3n+SGjY6+hB6pryCTyRh79Y2MvfpGRFFkzOvrOVoneX58nePh7BJ+qZQSacvH9vF6YVYUNLDi80MEhOr4+u4+GAWBcB9Jjxlljby0LAuA3/eVkv50x96jX3J+4fnt0gT36cRPGR7rGTLJX76exbvvZv6Qh1C4RMTnnie19isuufk+9l52Xodj+8IbhRV8XFwNwK5hPXhtUQaL08uATApemozY2EDRDTdgO5xJwrffoB/sTT0+rnsE47p7V/fsWlLIrqVHALj943FUVi0hI+MeAEaO3EHGmq1s/uk7Bs+4mKEXXsasbrMo3L+H8CKB1OE+2CIX3w0HF0jbPrxUloM11M2TnnXo1ano0v6aC9zpbKKhYW8LzbY0OX05JIjtsSIX561HVn8FMqBIX0R+0CHGl04h+K472L9iKXE9ehIW3wmDf3fSeryNzV5JfNy1OCrNmLaX49c3Ak2nkzM2Gqur+PahO7E1mznn5jvoPcH355yxYQ1rF/7BAlcP+iT0IrVBSrTdu6LIy0ARBZFfXt2Nqb790NNPP/1ETk4OERER3H67O29qS30TF+2X2Ih/6xWO9eBViKKT/PzXGDN6v8cYypAQok5BHuHQhhI2/JiDX4Ca6187Mdtws6OZp7Y+RUFDAR9N+IgovRRmbuswr6ysZN++ffTq1YvYWO+KJ4ulhMrKRYSHn4de7/k+ytpWztpvpe/UpY8PItxHObSsTSjIX6PkUGkDPWM9vSNhaiVhaiXd9FouiQxmcXUDT+SWMHlPLr/1TaKzn28ad0OIlvHXdGfjwn0cXF/C3Y/cwytzcln30ZukdvuQsJCgEz6j/2U4nU3U1W1Br08iIuLU33//FJx1VTyi045c1eJBaVNmXO+QsuVtguDRP1Gn4cfeXXgoQcsYx0+4tAd5cmoqL1zQkzvTupK0MwnHSgfleQ1UzGvmwq0mrjHtY0qGlFmekJCAyyXgp3PH/I4RWcnlSlJSnqRvny/RqUM49ntuW9rcFkG6jplAtW3KxzQ+9BlkMiVwjOlW59UO4HQJPvefLDI2l2Gqs1GWa6SpyuplnFjS0zFv3058sB+xLcqmd084MdNnW14VlcLb4Cmo64pK0NCvdCI9ShSMPyDStbSYD19/6rTuI7hNPFwrlxHm7/nsLQcPYmuh4a98/oVTGrv6qGdlSFOjmyTLai1l5x+/4LBa2DL/Ow7kHuX9Nz4mbqURbZaRH5/wkfzq6ri6S3S5J6W2FSKni/QDN7M//QbWb3B7shoEgTo/BZ/1ntBK4Vajq+G2HvczZ84c0v/8gzVffsw3D8zBYZcm/6io6XRKmI1crqZ+YR7mbeWt5bUnA2NlObZmKayUuXm9zz4Oh4OVn7+HvayACyqXsKTGSNSoKHqOjmX6XX29+ovQmhDaHo7p+lRVVXnsPyYTAXDALKBQSCtWg/9f56DI3ycZy20ZWTvCjvIdrDiygtz6XO7f4DthevHixWzfvp25c+f6bD+c+RD5BW+yfcc5Xm0Wk6N129bsowKtNr9VX6rB4uD1FdlMfX8z76/NpbrJ5t0fydMyPSKIVQO74SeXc8OhQuxC+59FZHITXXv2ZcuveZiq7Fz64KOonBY+fP4VhA6O+1+GKIrU1W2hoTGd8PBz0etPj4fnn4KzzoOC04FcJU3ezhYPSqbJwv3ZRxkXYqC73ttdOS40gIXpT7K2eC1/5P/G5O3jeTRmGVZdOHIm4nQ6qS2Xstu7lzWiUGSh6L4OhWEq9VkaPvlmPTHJQUy+YysymQq12rsKp5NOw8qB3ai1OxkZ7NsV9+X1g0gvNjIw0Tc74KXdLqVTYCdi9DEkBHhnaPv7d2PQwN9xCVaCg7xZWmUyGfNvGcaeonompflmaHw1JY6JoQEMDfL36W7uMSKGksw6DKFaAiOPUxnOzOTIrMtBFAm6fBbrn3gSh0vAT33ir+EFSRcQ6RdJhF8EScHeP7r+U7tjMmcxrGkYtTY/5GnZCBkZ9HinfUr8jnBHQgSDAvUk+WkIV6t4YkoPJveKpnuUAblcht/gwQTOnIm9sJDYd989pbFHXJJMQLiOLn0kIsBOnW5BRCTA0IvAgD4MmnYhm+Z9zaAZl/D14o0ECCJolWCxE+Rv9nb1T38PUs6DzqN9ns+vbzhytRy5v/qkvRMdwek0ee2bEh7IFqMJf4WcPn37EhIczNOj3bwtWn/3CtsXDbc6zoC90HfJd3uIT+vN2Gtm47BaGDTDt+zAhg0b8ItvxpSvIdNf0u/ZHCGyxs/B8NxiPu7RCZnggp+ugNwVyC/4hAsfmEFFYQNd+/vmtrnooovIzMxk6NChHvtnRYVgdDiJUKu4IiYUW9QyrNZyAgJOjs6+Iwye1gUoIGnAyfHt9IvoR1poGhm1GTw6WJI+EATP7010dHSHIopabXS7bX3Gx6P1UxEYoSP2+EqjfT/AHy2epXsOsbdCw+4iyeu6JL2M4V3DyK1sYniSb09epEbFZz0TmbQ7m+/KarkxznfOlcVylOEzxlCes4cVcw9x6WOD6DTjOsp//4wvv/iJm2afYqn9fzmamwsxmbIJDh76t5Be/idw1iXJPnP99Wjik3no6Uf4ZM56xlzdjTtVTYjA4v7JGI4rcRQEAVEUmZc9j9d2SZwb3xXq6Yu0ev417mnCUwYyfPgIDq4tZX36zxjNjUSEH6HEv56C9Bvpb/HHX5T9z1QqiKKI2ZyDThePQnHyxE11+/fzxzvvoLHaGByvQfPk85SYShga/fcmbv0349N35yEe3kDU8H2oAxpRBziYMD7vP/q8rNYyamrXExE+CXWbigmXKHoR9x2D4HJRkplBeKdEn/kioijiarSjMKg9QgN/FVu2bGHHzu/Zro1nZ5FkKER3D6Gwk2Q8l43tg7z+CLzX133QPyiZ+0zCYneRXmJkaEuVjiiKGI1GAgMDkfsgCRQEByZTJv7+3ZDLT0Ex9xiJIsBNa7BE9OP+n/dzuKyR724cQnyIHysyKhiTEo7V3IRWq/WS0AC46VAhBc021g72JsUEqKlZR1jYOBqqm5n/4i4Se4Zyzo1pvPT48ygL9jDuoRcZ3N+3sOL/EgTBRk3tenTaBAyG1P/otfybJPsXIXc5UKg0uOySC3CvxUqm3cqqgSlexonVamXu3LnU1tYyfdxgfpcP5bv8GPZRiryxgVWlXQmoPsgF191F08oiYnKNRPhHYrLW0a37Zj5Z/yJNaiU71Fa23vT/p5xp3r4d8+bNBF91Faoob5KiksxD1JWV0GP0BJSqU5ctP3LkAwoK3wFg7JjDJy33va62niOJiexKTOWDCDnWpTfgsFeQGpLKgmkLfB5jEwSyzFbS9LqTInQ6GZiN9RxYs5zEPv2JTjo5Vdz/BA42NfNH/x7csXExEZuNNFzuQi7/z7NAarUxxMV6r07bM04A5AoFCT3bZ6KVyWRevEFnAsOGDaNUFsDGBjvqxjqCm108NCGZR46UMyRQL4WjghNh2B2Quwou/PSMX8M/CcV1zdjahLCsDhfnBssRRQGLpQidrhP7mix8dLSKS6NCmBTWm1yzlXC1k6AOyoCb7U7eWZ1LsJ+aW4ffjUylg6jeuIK6o1XAR1dKicDNDim/bWJqJK9/sxTj1j/pqUtgwoyZRE/w5K6ZGBrAPdXFNDldXu9mh6MBpUqaAAPD/Rh3VXdWfp5BXPcQ7n78Ad64Yw5/vvsa3d7/iMCA/77k0JNFkykLq6WEsNAJyOX/e9P5WZeDInc5UGk0OOxSzsk2UzMjg/zpZfD2BNTW1lJbKxFJLd+wna75Cxgj7kAZkEq2XiIBaqypxlbRgGljKY5yM2OKOmFv8WaH6iSiI71GQUxSEAAWp4VP0j9hacFSn9fnEkTm7zrK8kMVvm+gqRJWPO6TSRMkqfeS2+dQ+/kX5I0d59VuNtbz8/OPs+qzD/js9ut8jmE21pO9bVNrmfTxaEtYJ4rtxMTtzSB4smDu8QuiXufP3k7dKNAlUxYlabfUWGp8jwHcdOgI5+7OIW7DyecmnAjrvpnL1gU/MO/x+3E5fTO4AmTUZJBbn9tu+9HaZlbmVbPTaMKXI9LhaKSufhuC4PBxtORyL89vkLSdfODL0hr2OuGGOx7G/510xo7JJka3hvJ8aYXvcgpkbSunsp3QSHNzM9u2baOystJnu8PRSEnJD5jN+T7bXSY75n1VCM2+rx9g5x+/8OW9t3Jkv8Qm67BZyd21jebGBkRBpDzPiNUsHS+6RJo2lGDaVubzeflCidXe7vN132gdVGX6bJLL5XRO7QxaBfbB4Vx7RS9mxoeRPaoX3/buInmiRBH6XgG3bpKE8/6hcLlcNDf7/k2eDHRqBZcMjGdMSnjr/8cq9Q4ffpBt2yeydl0yz+WVsaS6gWsOFvJLRR2jdmbRffMhCpp9544A/La3lM82FvDq8ize3lQBox+ksUAkZ+AgstJ64jIaeXbbswyZN4QZC2egkMvQ716In7mafcZcNq8qYdW2o6zLrmJddhUbcqqRm50giNQ6vH8fjY3pBBjcobPkgZGkjYph4/wcbEaBGfc+jMbWyHsvtq9I/t8MQbBRVbUCuUxFePjE/0njBM5CD4pCkAwUl0NaReQ67EwO9o5x1nw2l8a33mLK+edTPGok50fV4FweyzBRQHvnXVQcKWDNl58Qn9YLbWQgmpRgbDn1BF+SwiUGI9UlP/HQwPfZUDKMp6/8rHXcn7J+4sP9H0rXIlNwXmfPDOtF6aU8/KuUNPnk1B7cOPI4ltINr8LuL2DbB2ya+Q724AQmJLQpO5XJ0CQnY0lPR5PizaapUKlQabXYzGbCO/lmQP315aepPlIA+OZF6dr1YWxE0yl6DEqlj5Lbwo3wTQuR3d0HIFhKEL6oRwrXOBToBRtmUcV9QfX06PQy5ya2z9ZZfAJ6/7Yw767EtKUUw/gE/HyUjx7DMeI8aJ8gb0f5Dm5aKekCPTv8WS5MvtCjvcZk49z3N1I/NByKFaT5a1kzyNMVvW/fVTSZJIHICeO9jYCtv+WRvroYgNs+GudBgAVwYUQwCyvrCVEp6azTkLmpjA0/SuXaU27vTW2Zie0Lpc/pkkcHEnFcfsmff/7JwYPSd+mpp57ycuPn5D5HRcXvAIwbm+31kqv9LhN7USP1QNwro9z77U5MLhexSjmb5n0NSN+Z++cvYdXcD8nctA6AkVe9x+6WaqXZ74zGmVVHw58SVbqzzopuXCTrvpmLUq1h/HU3I1d4rpKNDifjdmbR5BIYEeTPr/18JPzZTPDhEDBXQfK5cKW3Jy6Jw/wen4E6dAYDgn2wMK94FHa0SCf8Q8M7LpeLTz/9lKqqKvr378/06dO9+rxaUM7bRZXMiAji07REz8ZVT8Ger2H6+9BjBgdWLyd99Z+oR18KKeGYzG5pgFS7me2ABjhicf/+KmwOurRTVdM7zl2dMyFVyl9r3r0Hk84PuSBQtmoJ25AKBwoapO+sPK47+pr1RMo0DENFTL8Y5C0cLk6XwPzCauT1Nvbm1XKkJU9NJpOhkMmI1DgJDfX8vo68JFmqIpx7iEseHcT+866g7s9v+fa7X7nmat85Sv+NaG4uxGzOIyzsf9Nr0hb/23fnAwrB6eFBaZIJPhkM6776isogCFr1Jxe+/RaC1Un5ijREu4Dijb1EPjwYzUX3YdGqkKuUNMzU8mvuVqZ0CqZX6FTeOpTJ4Yoa1h4dxb1WJ34t6sRdAt3lep0DvQ2EcH+3C79TiI/8jjYrvIf3vEGTQs713W9gZNUMAsJ0JIZo0PS7G//JAqHXDMdisaDRaFonJ63en1m33Etd+n66XnMdAIU1ZiIDNO5k1ROsbt9cU81nG7sBFRx5xUeHojbaGEe3txooAwL1ZIzuDUe2IH49UXKvT30HurQfZvosLZGl1UYv8jxfaFhWgNDspO6HTPzaTKjHY8RlV5MydCTB0TFek+IxmB3m1m2jzejV7nAJ2AQRWpSMM0xW7z5O7+PaormhjfElioCngZJkM3Fv+gbp8xuUgqoNAZdap0Dd5m+lD84avb5jzhOl8gQxYh/+1Wq7g5E7smhwurg1Ppxh487h0LpVjJx1DYCHR8rS5Pa8CE4RZZg7aVrTNYjMzRvIWL+65fpVjL3GUyjSLohYWqox8ttbvTssbor+3JVezTZ7Dfv2X40oOrGXvQej93mPUevbg/RPgtVqba0a2rt3r08DZX6F5LH9o8rIp20Lh2xNsKUlkXvBNfBMA+u+/gynw05RxfdcNnkEPVJfp6JyIdFRF7L7w4XMctagdSm4bfSLaOUyuvvrGN4meb+8/DcaGvaS2PkOtJooescFkfHsuSjkslavTPGsK5kx7DwEuYLpR57h4hoF++IH0aVAyb7lixk/aSzL9q/H4jJxIG4XcVr3b1apkFMkcxEYqWdmz2iPhUSzzcrKdEg+rg5AqVZw7uyeLHhpF5vm53DtNRfzYsYBypZ+T3q/XvTp6Vv+4L8JFkspVmsZ4eHelVX/i/hLBsorr7zCo48+yt13380777wDwNixY9mwYYNHv1tuuYVPPvnkr5zqjMBqs6NAQK3VtnpQNGolNT6E2jbfOpS3kVRkX1v3JzUFVYwTJAp8UYDFB8p48JcDADx6fne2ND/Hvqp9fJ/5PQevPUhs/XmsySvjfIccf8H94xoTP4ZVF69Cr9JjUHt7H0Ymh7H6vtFoVQrign0YKP2vhuRzWF+QTdP+OwAo22RjV94RAKYkBaA0OnEZYceOHSxfvhyAJ598EoVCgWA2U3PbHMTmZnLeepfdX/3Jk39Iq/x9T55DsF7NRY89x9GMA3Tp513pA5BZfoJqi8GzoakMQpOh96Xe7bYm91Rsru5wqBS9lhS9Zx5NZWUlJpOJLl26eLy4/AZGYtpYit9A3xVIxyCTyYhI7Fgmflz8ON4Z+w5apdanInN0oI4FNwxhQ1UDIdH+zIoJ9erTt8/X1NVv9pJFOIZRlyUT0clAXPcQ5D4IwdLT06lrEbQrKioiZXBXAkK16ALUBEX4EZ0UREiMHkOoloBQ77LxSZMmkZqaSmRkJHK5nD179rDxSDHhfQdwWec4kpMeJTxsAgZDD58rsbCre2DNb0CbHNS6r9bhpMEpGferahp55ta7OfdWN8napJvvoEu/gcSn9UajDyY4yo+YpCC0/irwVxH9+BBkChlyPxVx+jRUGi0Om5W0sRO9zh+hUfFb3yTyLDYubs9A9Q+HK3+GygwYdKNXs0KuQaHQ43Q2YDC0kzA59S3Y+x10P322378ber2emTNnUlpayujRvqu1nuwaw0dHq7g94bhKH40BBt0Euz6H6R8AkDZ2IumrltF1gKRSbjCktiZYqrqrWGOU3huX113OHZ08q5AcjgYOZz4EiJSW/djqHdRrlOQb8wnThRGoCWRlSRmCXHqHrQwdwTb5tzyyLYWKilzWpn/KfT8tRrzjfgSn0+vztwsCCyrqmRYR5OXl3Jq9l3FpvsnYgqP0jLm8G2u+ySS2WzB3PvEw7951O7+/9Qpd3/8Qf71veoX/BtjsNZjNOYSFeYfu/1dx2gbKrl27+PTTT+nd2zvxbfbs2Tz33HOtf/v5/TWJ7jMFc7OkSqvRanG2eFA6GbRk+lCrLe8WCi1ezzVb1uDv9KdWVklUZQVVJUUMvMydTJcQ4kettjv7qqTVWdPGElK0KqY0S9wZap20oqiwOQhQKlpJk9pDlwAdMnU7gmkAhij6xQQy6/tHEZ0wsHsv8pEmel3/CBwri9B0CaSoyB2XFwQBhUIBCgVyjQZXczOq2Fjyq9t4CiwOgvxUqExmug8ZgcyHRLu9uJg7ClbROa4fF0xvR1/GLwSmdVB6m3IuXPodKLWQ4iZoqysrQetvwG6xkLN9MylDRyLbb8VR1UzwBUkoDGqMRiOffvopgiCQkpLCFVe4kzWDJnchaHLHhsfJQiaTeTO2HoeBiSEMTPQt3Aig13fxIrhqC52/mr4T2xfs6tOnD1lZWWg0Gjp1kgTXoltymY5do1eJZxvI5XI6dZK8VzabjV+WLee7YefiPFrLqxUNHBzRk5CQ9pO35X4qr1BZd72OT3p0otzm4IY47zCaxk/voefUZ3y8R7vC4OaTMYSGc+tn36HSaNsNtQ0O8mdw0AmSHJMmSP/7gFJpYOiQ5dhsFRgM7YjmBSXA+Mc7PkcLPt9UwKbcGp6a1oOup8nIeiJG2PbQq3tPeiZ2R+HvO8wyMzKYme0ZclPelP5vwcSbbmfiTbezIcd7gRCZEgk7pe3ipmL6hHsaKEqlPwZDGk1Nh4iJdi9A/sj7gye2PAHAspnLmB4ZTOX2RVRoyjnsWAtA2pRzqPgil+DoGGQyGT1G+Z5svyurpcru4CYfJca1TQ0E6oN83yfQfVg0Jdn1rJ+XzWWPDWLSHQ+w8c2neO/lt3jshZP7nP9pcDgaaTDuIeJvFK/8J+K0DBSTycSVV17J3LlzeeEFb5IqPz8/onxUj/ynYTZLbnitVouzpYon3qBhs9nbfXx739uxV8upzzQQ7BJxUEtTcz6u5jLKNRF8OW8vIRol11QqMS0p4dFHHuXatGsxHBBpWFRIBHDprGRCRsWiUMhZXGVkdsYRAHYOTSVB5/sl07yvirr5kmUU8+ww5BrfH1FguI47H56Jw+YiNNYfc4MNlabF7T9emvQm1MSg1Wrp1q0bqpZqHblWS+fff8NeWIjfkCHcY3USqFMxoFMwncP0VL/3HjUffQxA98zDXi/Sqvffp2n3Hsa7VtF71q+n8PTbwFwNOcshvFurgZKzYwuL35KYdkPjEqgtOcqmeV9zWeeHASjPrCPupZGIotiaMNm4bx9lhzKIfv45ZO2EahqtDvYW1TO0S6hP6v5/MqKjo7n00kv54osvePHFFzl30rk4s7cQFZlI8uSLT2kstVpNp4R45C3PLqg9xeCTwAUnCLdZTHbsFheB4TqOHDlC5uHDDB4yhODgYOrr6wkODqayIJd5j0sEYle8+OYZr6aqW5BN894q9MOiCZ6RhEbTPn+Iy2THvKsCTdcgNAnth71qTDZeWCoZ/RPe3HBa+krPbnuWX3J+YVqXabw06qWTPk6wOal8cw+uRnvrPeVXm9Ao5b49rW2PFUTeWZ1DidHC09PSCNS5Q6oapZz12Z6Ec9Gy8cyMURCiDcHg7OdhxPipFQxKDGHQwN9wuZo9ctBKTaWt2zXWGvql9uOFmBg0DiON2kcJ00cgk8noO2lyh9dbY3fy9pFKLokKIeU4Xqq1B7cwovuJeWVGz0qhsrCR5XMPcfHDAzgw9iJM6xaw4OelXHrJP9dT5gsul4Xaug1ERpx96synZaDMmTOHKVOmMHHiRJ8Gyg8//MD3339PVFQU06ZN48knn2zXi2Kz2bDZ3AZCY+OpkTWdCo55UHQ6t4FikkGgj5e10l5F5S4LYenlbIhKRRg0nKiq3QxtOJ/sQBnGlsqGrwb7c/POJmk16x+LJaSWfckb8Ov8FQbNCMIV3wJw2OT20hyx2GlyCbxeWM55YYHMinaHB2yF7iQ9V6MdefhxH5HLAbu/BEMUAT1mtO7Wty3RzFoGDcWEDbieGTNmcDwq/Bxkx5gZg4sgPzX3nuOOzVqzsr36t8Xynn0pafksVYcPM8KHu1kURXYW1hFm0PheZe78DPb/IG0HdYK0C2iscleaqNpwIhj1SoLMTr70F3gKCA4O5sYbb6T4008JXLyEBiDk2mvRdvOML6enp5Oens4v9fEcKHdT9wsugYPrS1HrlHQfFtX+SlZwQfqPoA2CVN8vhgyThQyThRkRQWjkcux2O9988w2lpaXMnDmT3r17U7hvNwqVik69+nocW1ZWhl6vJzAwkH1H61l2sJzLByd46aTk5eXhcknevhUrVwBwyWtPkXvoADH3Psy8zHl0C+nG6Djvz8FmcZKzo4KY5CBCY/255ZqrmWhq5qjdxdgQA067i4L0aqI6BxIQ5u36rqurIycnh7S0NC/dmSPpe2luMNJ95BisVhu//fYbjY2NXHrhFSx87QC2ZicRQxQcrXmUa62Z1O5IYHnKHeTmSpPYzBFuaYDCfXs8DJT6+nry8/Pp0aNH63vDWV2NNTsH/dAhXp49Z3U1jooKtD17SsJ4gkjzfmlSNW8rJ3hGEkajEYPBIHkRj4NxaSGWfVVAETHPDUfejvcy2E/N8K6hbM2v5d6Jp5fP8GfhnwAsLlh8agaK2YmrhUXWvLOCrJ7BXD53OwCfXDWA83pKKsFOm83j9wOwv8TIe2vzAFibVcX+p9xey2OcKMdjfPdZPvcfM2ZkMoVXgvx1adehU+pIDk6mX4SUKxcQGAgEcrLylkaHkxsOFeLfXMXkkGbA7WFstjbT0NxMXGjHekMAaq2Sc2f35JdXdrP1lzxm33wVz2cdIv+3L8jqm0b35MSTvKL/LATBQXX1SiIjp52VXFGnbKD89NNP7N27l127dvlsv+KKK+jUqRMxMTEcOHCAhx9+mOzsbH777Tef/V9++WWeffbZU72M08KxEj2tnw5nC7V9leAkUuedpFl09GPO2ZFBUEM9Eao8dpcNo9PRbGxNW+nl7E9m56HY9BoqO/kRH+rmFfhB3kQn9TwAmixbWvfP1snQKewMztewbsUOvhscQF2wmuU1jQw3LiDSpkLT61oM4+NxWK3YA+weSYWt2Pcd/PmQtD3tPRhwrWd7TR781CJ7vuMTuMszKdDmsnH5sstpsDWgUWjYfdVuAA6WNPDaiiwmn3s14zslYB8xjmUHKxjTLRx/jZKy3HpWfXmYZT1C6Q0IInyV6eTJXev55KoBpES6X1aL0su4+6f9AHx/4xBGJh8XCkgcCRulEmPipYmq77lTQSYjKDKaxL4DqCrMI6JzElM/2EaOuQmhEY6R1sfFxRE8eTJHF/6BIiCAJa59PPvNRcT6x/LnhX8ik8lYvHgxTqeTYpsOcBvHubsq2fyzVDpsqrcyaIo7UdnD9X7wF/hjjrR93isw9DaPWzA5XUzdk4tFEHgwu5iiMX2ora2ltFSagH///XcMLju/vyp9t0ddcR2DZ0hej4MHD/Lrr5L36cYbb+SOebmUGi3M3VTYuiq32F08tySDepOW4cndaairbi15B5DXGfni4BfMPSjRlPvSXtr6Sy6Ht5QDUhWNWqskyaDnWC3Mpl/yOLRRut5b3hvTKpx5DD/++CPV1dUsX76cZ555pnV/bclRfn1J+jQOrltJ8rRLycvLA1HOykUbsTVLRlbBgWq+79vED0Iiu49UcKm/mX6xAXQqbaTH6PHUl5ZDgY2u1T0QLE7kOul19MMPP1BTU8OSJUt45plnEF0uCi+7DGdZOTK1mu4H3CXnLpOZgmnTcRmN+A0eTKdvv0EmlxE0oyvNe6sIPLcTmzdvZvVqKRn3WC5WW5RXmAlq2e6IJE4hlzFv9lAEQfSquDpZPDbkMX7N+ZU5feec0nHKEC2O6UGo6mTEnNOdPZlug76wRmIXXvDco5QcPkSP0eM5f859re1dw/1JDPXjSG0zd42Ko6amhrAw7/BcScn35Oa9THz89SR1fcDdILikd4lMjtNwgYdHxWxzom/j5e2inorLTGsfQRQZdhLeS4tLYFm1kVcKK3BYGrlOaSLaL5Lfd6xixqAJyOVylu3bxLSBJ59/ERbnz8hLk9kwL5vYbsHc9sRjfHLv7fz46ks8+uEHaNsRI/ynQBQFqqqWEREx2Sf78tmAUzJQiouLufvuu1m1apVP5j+Am2++uXW7V69eREdHM2HCBPLz8+natatX/0cffZT77nP/mBobG4mPj/fqdyZgbsk18fPT4bRIRoVeq6TJh/aGml5EdllFRlkc9d360kVwEahXEuZoguY9nNfQyKreE3A2LOPxppVYl79GYHE89sCjhC1y0XAh+O1UwHhwVFZSPWUK5zQ3U596EZaYsSQWWqkLln4goT89iUbmQFz0KOIjJfyy7hWaTEYUW8K4/6WvPS3nQPezsRpDUVQ1o4po453SBoImAGyN7VKfHwuRCKL7vt9dk8um3Bo25ULOCw9y4cdbOFS6F5A8D4c2lmGqt9E5T8eWIePpLNOyeoP0EjrvnY0UvOx2m1rsbv4Ts48E5JronlyfNoQicxmfNhUxzFyNwpBAasxwVFF6lCoV0cndsWbW8dCABL7Jq+KqIQms/vwj0lctY/Id95M6ahypB6Uk5ddXzgZaXMwCWI8Y6dGtBwcyDnBHbwXaLj05Ly0KBIGAULenKTzBQJ3Zzq2fbyC57hvi6ksJDjFw0SMLUPm3WfMFJbD26FqWHnyZiTo/Rgx6EaVfD7RyGRYBIlv0hiIjIxk+fDjl5eXMmDGDhuIjrUNo/PxanrukhNv6fMxmUqMDKDVa8G/zol+dWcmPO6USZGdqVz6+9SKys7Nxbl1C0LMPEHPJ9XTKX+S+Fz/vNapG7za8fU2oJ2Js1ev1VFd75yioNP/H3nlHR1V2bf83fZJMeu8hCSEJvYTeq4AUxYaIimLBDnZFxd4FRbB3sSFIUaT3GiCkAElI771MZjJ95nx/nJBkmID1eZ/3e/Vai7XCnH5mzrn3vfe1r0uNTKHAbrXiGxpOcst+ghQb2VdzAzUFe5DLfOnpUcmKuAzc7WpWFz1Jva2Gl45msc4WwdHoIJacrWRJ4hRkOQXoyhpQbi3C9wrRk0mpvGDgEASEVnFyIVic284Fixm7XpTeN2Z1eBpphoSiGSJKtVendWgK2e12lwClRCYlJdUE9QABAABJREFUXWdFb4c75b89EPzZ4ARgZtxMZsa5duD8Fo5UHuH2PPHd+k7NO8zoMxaT1Y5GpWB6n1CsZhMVOWcBOLt/t1OA4u2mYPeDYykqLuGrLz/n3X0wc+ZMBgxwJpqWlH6Ew2GiuOQ95wAl91fYJhobTux3GmavctrOYLGxOaOSfpG+Lk7f2VUtaI3WSwYopUYzE47norM7mOTjzuDWKu4aNwuZTEqEfyhf7/+FPtExhPj6oVL8saCi56gwynOa2P1VDtc+mcLoOxaTuvJ53nn1bR55+uE/tK//SQiCQG3tFgIDJyOVuk6g/yn4QwHKyZMn2/vwz8Nut7N//37effddzGazy8M/ZIjIEs/Pz+8yQFGpVKhUf7+CZFcwGkUOioebG1atHZlCSh9PD1aU1NBqs+PRqdQTHXsdpQN+JcjHmyqFEiUWbvfdioevEV2tmtv9xxGXv5ayQDFDkbaniEitCgQVvhI75jUBxM0Wr9ferMXRlr0pVwYSbJcyPc/GtAQVva2vI3UIIAO7zYpDV4/VZGL74Bp6lRaz/PYUHlixD+n5ltHuk+Cek7Qc09KywwA7ThLy8CDkbV0cDrkvjT5fYiutwm/ATC58nFUyFd9O/5bcplzGRoxt/3xKz2B2ZtfgppAhk0q40Gur56gwKs41McrHnden9MPiELil/DiHCxr4+KZBTuteMygSjVpOiJe6SxLpmfozFBuqQCJh1YGlDMs5gQQ42m0caZsHcijqBDIPKc9m3EaC3ZOXxkRg8pDy2PE6olQhbHn3TZI6kesW9V2Eyahn8FkJ5Uv3IhHkDCaQ2c8/hfT8i7E+H96dQKipmZvv34YkfADuXko++XYLg4+8h0QQUCVYqSm28M2V07hpy2646ygo3ME3mrfXX8bjm0rwPiqjOGIOfXbmsiOlBwUGM6Pa2i+lUimTJ3ekz318+nHdopcQ9uvYlwPvFr7OpIgf6GO/nmmjJ+MfHUxcXBwfJAiUNxkoNabR54s++Kp9+WLiJsK81VRqTSwaG4dhzzrCDu7BYLmB/YEOUoxmZsXPom9gX/zd/LvsCBs6O47onv74h2tcsiMAI+bEE97Dh6Bory6Xz507l6qqKiIiInhtaw4/nCjj+ZnJTOkVys1vrMZsaCU4PBhejiACmOf3HMsbRuDo0Z39lhB6N/gT2HQaf3kjgufTxHzuw0MOKXVu3syvjmHthDCGxL9CoaKchUG3cD+LAZg/fz7l5eXExMQAIJHLiV7zNcb0dLymOfMX5H5+RH/2Kaa8PHzmdK11MWnSJDw9Penevbtr8AOMvj6Rswcrienz11ye/5NoMHVkz8r15UilEq5N6Sh/KFRqLrtrMSWZpxh2lavKr1QqwWrpaIXXal31XqKjbueD/KN8JNyK94EsckaKJTP8YkGmFE0p413tOl7flstnh4oBOLl0Iv6dSLw2u4Ciiw61zvBRyLEJAvdEBhJdmcacUVOQtW3j5+nHvFHT2JZxkGkDxlxyP11BIpEwbn4iP7yYyraPz3DlQwM5fXIG5sMb2bC5P7NnuHaP/W9AXf12/PxGX9TU9Z+CPxSgTJgwoV346TwWLFhAYmIijz76aJf13fT0dEAk/P23YTSKGRQPjRtNVitypZRpwT68XFTF1notc0I6BlOVuwdxEydi3NjCuUYrEqkBNSJXxsPPTFRNIUHVjWQmjGbxPbdTUGOlWtvCgHH+RNd5E9OQg3CZ2Fqt7pFA+IoV2OrqcO87mS0fnsXdS8ENl/WgqfkBfthTToCpmcH+pQS6uzProRc4s/Elbt+ajlRoJX/8BBKOHe24kIB4UJcCJeL/O83ozMUtmErkQCRNa88R/IBrO16UV5SLmeDVgyK5amBEe7bm81tSOFLQwPhEkVwYnuDLgldFm3e73Y5aIeOb25zN0s5DKpVweZ+L14mHhQ3j2h7X0mRqYpnOTr6jkg32Edi1kOORT4G0EIywLHIVU0uuwbTRChshy3sQx30HsWWWN4Ld3k6MHRA8gDfzB9GyNwdJ/46ftKSzrkjZUTA1i9/fua8gSTx3+9GNSM7rvjgk9LoxD9ljPuL/gzp8LUb6+aPMLgZAUS5l1Z27ufyevozp1XUN/zzUFQoaDXaeMrTgoCc7ip7lk8n30WN7ChGviAGsTCoh2t+DDw9uRUCg0dSIVKHl0GPiYGA8lU7emmUEh4kdEqPqpNzyy062X305Md4xFz22VCohvMfFCa0yhZS4/s7kUVOrHrlCiVypRKVSERMTg95sY/VesZV00Tfp3Kz+iHvvvZfg2FBRvyVhKpz7lXPK8QT20lKjtIEFFIKCWlUYuf1fQS7ToAixEfiSkmqNB/ZwD7oLdgoVolndx/mfcv8IMUBxc3Oje3dnh2tVfDyq+K6dWd1TUnBP6WiJt7daKc4uIL+hhKFDh+Lt7c2UKRfvfvD0UzNk5t/T/fWfwtSYqVjtVryUXhftLkseNe6iXTEAPXr0YM6cOcjlcpKSXD1bIiLmkd8wFBp17a3kAAQnw5JsQAIerr/3zpk/+QVigA5BaH896fbsQbdjJ/4LbkbV6fv1kstI8fYgP/sgC0eOw13lzFeUyWR/Kjg5D5WbnMkLe7H+9ZMc3VDAortv4fm8M5z59gP69E4iNib8T+/7P4H6+j14ew1Aofj7vej+f8MfClA8PT3p1ctZS8DDwwN/f3969epFQUEB33zzDdOmTcPf35/MzEwWL17M6NGju2xH/p+G+XwGxd2dWksjcoWMKDcVw300fFhex5XBvk7llJiYO2iasIFr1h7iiNbAWm0f4pUGoqc8TND3XwHQ51wJ/YL70vdhAYdNQKaQot22Dt2eMnhXT0SbkJnXZeIL0g9Y9G7HoODl34uXr3yRofVHeVqTwAm1Dwd+zKFv3QL2joHxe+/Gd948l2vxHBeJIswDRZA7ct+Ocpsq1ht1sj+Wch1+N/wx46jO1x7kqWZWP9cHd//+/ezevRsvLy+n0tyFONqs58m8ckb5evJMXJjTvpUyJUuHioOtsa6OJbsSibfFYW4RiO++hmqpnWazG7lupQSoCumNWNZSaZSYWs1ovvqWnAW34XPdtYS2cSPchw6l4fNvkIX0Rd0zBb/rkpAoOr0sk2dDWSpIJDD19faPe40YhXbnCgzxDrwHaPGtTiLpE1f1ubsHv0jeXYux7raRbhVVZX/9IIs7V4695D31GByCuSCfnlYDWVYNwwNLiNu7AkWIK2l8fvJ8irXFJPknEa4JxyGIL3gQKK73xT94A0iu5oC1ghFbUuHqv4/Vb7M7ePTLgxxNz2FS3S7ufuVVAqNiAHEAmjs4km9TyxgmLwYgM/NnxvXoA9/OBYkMHisnXunOuW3v4mb8jsKCFALzDTQGXYZMsgsAfZCEHQukxCfqiTzVQrNlLefJH/NPPE/VSC2hcd6uJ/cH4DDaqHnzBCqDDZVUy/Ijy534M/8R2K1ioCb/z3EaZFIZV3S/4i/tQyKR0Lt3bwRBoKm6Eq+AIGQXEI4f7haCQxDdqZ3gEUB9uZ7aU5V0HxyMolPG7YGJCQzp5k9CsAZvd+dyhKMTr6vykUdx6HRo168nKcfZmiCoPpsyj2BCfH+fW/MfRXCMF8OuiOPQj/mE9/Dl9qVL+eTBu/ni5RdZuuodFF1IKvw30Nh4GA+PBFSq30sr/r+Nv/VbUSqV7Ny5kxUrVtDa2kpkZCRz5sxh6dKlf+dh/jRMJjFA8dS4Ybc4kCvFAeyyAC+ezq9Eb3c4mVJJJBL8vILxk35EjBeUlvfBkKWnbu8zRD3xDKbSs8y8VawLH606SoW+glnxs7BWutrRF9W3suSHdLzdFHwwfyCyFi1VTz+NRCLlm6ee4ZgpmrdDxQzOeRE5gO6HDyH361QmEQQ4/jEScwtuw+8DmfMLQaqUwfRa7MZSZH79XM6jvr6esrIyekZ4Id/1IvWpViQxg5FeMQuToZW30bC2qpEdxo3E158StRP8OmaXublil09LSwtoK0RPoKTLwTvC6TirSms5ozdxRm/iidhQlG0vqdzGXFakrWBk+EiuT7we7QcfsuhsHaWxCfRxl6Eoup4Z32RQHt2NR6flciJiK5GOeH5JSqQmREHR4B4Up4itx83ffU/osmXsL9+PI8rB6JNHkCgU5J+opSyzgX5B7iiUMgynapF6KHCb+Y7TORrPNtA3dhLKd65GppBgdxhRKLzbbrOA9qcNSBQKvC6fjrt7DH1v+gnHDQ7qv8mlukDLZQu6QfkJUd1X2nWNXd3dl7CQt9hQcQqdyh3vJRUIRhtSdwX1DXs5ffo+NB4JDBz4PYl+iayZLnY31evNzFx5kEqtiVXXDyDl/mV8+/HbIPkKf52BRXcvQbDbaV63Dpm3D15TJrsc22ypp7zsc3x8h+LvN9JleV1pMTkH95I8egLFdg/W5erALZxvw69hVvZpfD080e/bj2bMaF66ojeR1Z8jlVWjVutRKPdC1gjQiSRcSg4h7XEZl0+9n81fuNPaWkFrqB/9LLWcPXYrhzxMOMrV3JG3g+6V9zHK3ZOHfI+Q15a80mp8kUjE/1jNdmpLWgjp5o1MIQWHA46sBFMLjH4YFM78N2tNK5ZyPe59AnCYbTjavI0iHQGEhYUhOASKMuvx8FERHOM6K21qtbDjbA2jEwIJ8Xbl1gkOgcL0OjS+aoK7XbB9cyl8NF5snb9+rZOuzx/C9qVweCUMvh2mvd7lKs3NJ5DJNXhqOiwVTDk5VDz4EDJvb6I+/wxpFyWsC3Hwuy9J3bAWgCXfbW4PIBwOMz4Nn/C6vyeRYTc7TSpsVjs/vXESi8nOnq9zuPv98RjsDuosVqLdVPQOKsdqaUIQxjpt5xA6ErzuQwaj37kLt0EDnc4nrSADi1SJxOc/m8noOyGSitwmdn2ezbVLUxi84D4yP3qFd95YzYOP3fcfPfbvQXPzCVSqENzc/ndldP6b+MsByt69e9v/joyMdFGR/d8Ei9mEAwkqpQKbxd4uD55vMJPgrnZxzDTajOy0NdB35nJaWrzY7SswNOdlsv1iSM9J5y3Fe/D+29RfsZI7Mt5AQOCz05+xccaPNAZl4ZXUkcb8/ngZp0qbAdhxtobhp3ag3ynOLCPi4xhwv6jGaTQaiRhtR2ULYMjkZOTqC76iksOwpY3AlrcTbvnV+ZyN5WRkLAQESko+YOSIjk4ih8PBp59+isFgYCOwuHgLDUd9gROU/7CGQwnd+HL+o0QZK4lPbRN1eqe/kz/JpEmTOHToEAMHDoR1t0LpEdj6qIuHydUhfuxoaCHRQ4280wvrg8wPOFhxkIMVB5nmMYSmb7/B3y0Me/dpRCoVgDvWuHnEZv3AR9F3IXUL5qfhPhSqFMz1VvHDjz8Q/8D9RJ44QdCDD3K8+jh37xI7Im5IuoFFsfex/RNRGTdtWwnzb0mmaa3oX+MzKw7NMLH0ZKnQ0/ClSCpUZfoSeEsvpLKOl7t+926qnhCJgaacbIIfFgl1UpmU8fPbMlPvj4TqLFFwbukFhnybH4CTn0H/+aD2QiYR8KEVJBIkbbPMysq12O2taFtOYbO1OqV0z1XrqNSKAfXynefYuWQKl71yD+Y6FW5WO6afNtJis1P99DMAWB97FP+bb3Y6hcKCN6ms+gFK3mPkyGOolM4ci1/ffZO6kiJSN/7IHV9uoGeohjNVehb3UdJv8nRKb7wJQ1u3XlJONldf/Qg5ua/g5eVPctKL0FSB49wepJ5+EDu2fb++YdFQVEE05SxQLgcrNBS/Q4AxHKW7GzI3X2QC3CSNZofbWbprQ5A32bGYxLLClvcyKc9pAuDu98dD6WHRSwagYBfaBTtZU9VIircHg9zdqF2dgWC207T2HBGvjML/xmQsNa1oenlye8B4zh6qZM9XOYDoYXQh12TJD+nsyRXJwF1pm5w9VMneNWJgfvm9fYnu2anMUZ3VoYZ8dDUl/iNQK2QEe/1B1+mTohwBqR92GaDU1+8mI1Mkg/dMXk5IiEi0bV6/HkuBWH7LeuhD1ofXsSt4D1f1vpIHBj4gbmwzix1pFWkwby31pcU45AoEmfO7pbp6E0VFosCi1dJIXNyD7cskUglylQyLyY6bpwKrQ2B8ag5NdUYUaiNX2z9EqLczIXEDo1I6RBoFQUDa9vxHrFyJQ69H1qllvbC6iNqWFtLdIpmg+c/yLSQSCRNuSub7F1PZ/skZZi8extm0KdhPbuXX7f2ZOvni9hj/abS0ZCGTeVxS2PGfiH9U75LFZMYukSOVSrFZOzIoJUYL3T1cibpvnXiLJw4+wfSs5czdp+Ctaneumf48UR+shmB3qu3dkUggYMO9RBjUIAgk+iVSol1NlttcDhV3kEen9w7F30OJp0rO6IRAPEaOROYr8gN8ruwwotu7dy8HU3ezK209ekOHJkxrayFWazP4RIpdOgC9nA3sAORyj3Z9Ah+fwS7Lzwu2AZzpqcamlKEecDORgx4mxCxjSHUh1apAGkPbzv2yV9vXt9ts1GacoFeQLz169MBu69rrpVZnYs1P2fRMa2Z9z9j2FxSIEvIAKdUD+f6F18DuwFNfTvyJN7G0uR9vi5NSdPVMBj95FYMVd/Hy0Suo3jeGPhlHKSoqYn+Blc3S6/jqvWpUko6BwN/NH7WHAndvMdBIGh6KzL3jJSzz6viOpWoZyMXzUoS4Xoe8Uxum6gI+RDta2rIHNlcfHjLbTOtOfQXXfg03rIMnqpxWiYy4ETe3KEKCZyGXO+ufDIn1Z9HYOOYPjOC75CgMp2oJv2ss4XHNeA8PJGL1KuRBHelwZZtibGd4eHSct0zq+vL3j+jgIWlUcn65fwzFr0znzvmi5oLkgtl4YGAIo0auoG+flSgUXlS9+x25q5oo3uHTntWoyM3m2MfvoDl3ihSNTrw9goIgUyRSiZQs716cc1RxZ1+BDZoBTFOCKm0sSkkrIYiE884ePoCYwXNryyIOXMArRdU8V1DJjLQ8qiyW9lKetO27dkvyx2NkGA6pKOonk3X8/qRy1w4ct0upNgOyTp09sgu7fLpPhhH3w9C7ODrsfca8vpchL+1iV/YFAWsbHHbRgboit8npc3PP+yjcEUHhsUHthPrOsDs6tKLs9o7l3jNmIg8NReoXxi+edez23Y1W0sQnpz/p2Lj0CGSthcYC+HI2wtBZNMf3xxDbk8/vfghLRQX5BhO18g5tF79OGTdBEDiy9ms8vXYw6ZZobn51JAa7HVNlKxalhHqFhk8y5vP5meuZv845gyRmUMR7LpFInIKTRl0jJwrPoYrqS6nJwuUXlpX+A1BrFEy6tSfVhS0c/6WYe5bcic43mpNfvEtZZe1v7+A/AL3+HAKOdquBf9GB/x2Ft/8hWM1m7G2zBpvF3h6gCAhOs/zzUHQqnwRo1OiMNnzcFbT4qTgcNZr0yhrSakIoCg6gb9lIFmTXMv2Lh8gtWOiyr94R3px8qpPBU1wcCUcOu6zn69tBajzf3VRdvZEzZ0W+x9AhO/B4IAscNvBw7TpQKHwZOmQ7VmsjGo2zOqdUKmXhwoUcLCgm89wbGANkNJpvpZ9+EApgWvzt3HHtSCRSKUzYJZaTOt2X3MP7291rdQ0NdKu4B6V1PBZHAp2TkttOV3OsSPSQeWfHWfo4CtGeOcOo4mIue+EFwgNfYuuO9zA4Ijid1Jde+WcRtKWYNy1i/YJb0BtbqbbDVHdPUGnAZoSABJKSkigpKcHDLhKuLSY7PTwTWTtjLQ7BQbJ/MgDznh2KxWhD08bNCbqvP1KVrL3TCUDu70bIkkEIFnuXAYpb377EbdsKMhnKiAiX5QDM/wkK90A/V44Q016Dk1/AuCdEL5R4126BTwsO8vm5eqZ1U/DqBToHMqmERy9LpGVvGS1bizEBfte/hObTJ7B5hiBVKPEYNozYLVuQuqlRdEFCj4q6hYCACahUIchkrgH4uIXXI49Zh8yjjuqazS6eQeErlmNITcV9sGugC6Bvy54aT57s+LCNcCyx28n2n8BnYb3QS5T0KDuOUjGAyOK1fDGmJydCEoDLuVvRh2kB88VtN/tCcjFT7+xFUUY93c97KnmFweIz4LCC2puwko7B310pR3Nvf2w1BlRtNgCCIPDZZ59RUVFBUFAQixYtwt1Lhbu3Ev9wV+HAN67uy5X9I0hp6zg7lF/PPd+k0T3Ik29uG0KPoSF4+Krw8FbhF3rBb0WmgEmirUdlWnn7x+dq9O2uvp1x5kAl+78TM3rT7upDt7ZsjrZIgbnBAQ2VtGzbjs8Vs522Cwq8jD6930cu98LXd0j75269e9F9z25aTlYh3/QZsS2xpAWkMa1bp26n8IEQNVzMRF35IWvXV5CAWEauCwhg98pfWTBlEALwfNweFoT7OQXMdSVFHPtJDLhLMk/y4Pc/o7LWM7V1C2fqp5CaoMbHZqCli+FE5KC4fIzZauLntENMHzqF6afyGeztwfDfsjT4mxAW78OQmd04urGQsAQfbn7iSb557H4+fvFFnn77LWR/QWX5j8LhsGI0lvxjzP/+KP5RAYrNbMbe1lMuZlDEH6LQ9u9CXB9xPbqzOvqH9mf8VWMpqDfQL8Kbw9pW1J4VfIKd+cUOXrj+AQD29rFS+8hBbn7zacrKvyI4yFXS2eGw0dp6Dg+PeKRS13rxkCFD6NatG97e3u1aM9XaXNxyriaqdDravAo87rh0GlClCrwoycrT05MJyT0oPmLH4Ssnu74fCk+BnlYJPimQN2o0Ujc3YjdvQurmPOv2De0IQyISk1E73DFmylFf0CkyPimYNcdKya/TIW/ZSGaheHe1HuFMeuBVvk7KQ9ujDwMqJtPkDY6mN5BWF6GIjGL4qBHs2LGDkSNHirPyOw9iKMlFCB/CUF81Q4YMob5Mz6Ef84hI8hMVYd0TnY6vVMudnH6VYV2/+OR+l07Dd5WVcEJoH/FfV+h/g/jvEtjUpmOypWgLr45+tct1FMEdZFpb1utcnbqJUoWChwc9zI09b0QV6+qI3Rnu7he/BqOxELlGLE+Ul33hEqDIPD3xnCB2jDgcVgTB4RTohDy7jKbvv8dnZodacXhiMtc88zKCQyAsuTcVB/KZsb2a0mgTt/czYFTP4v7sH9jGGBQI9LLnQ9+5UH0arvkCAO9Ad1ePImXHfbgnKogxfp5Eq5V4K+TgLUfeSUlZEIR2/Zba2lokEgmRyRf3THJXypmY3BFMfJtaSpPBSmpxI00GK4GeKiITL749QInRzEqrDrfefiztEcG8Qc5aToLgwG43ouqU0Tv/d319PcUREfhGRIDDjuck12BWIpFcchDzGhjKXT3uo76+nqioqHb3cvFAnk6l4NHd9vF9aiwhUh332MMobsxAQMyYllikLtk835AwAmNiqSsubDeGrKneyMSILxlWe5SHzFfRb8kNpBY3MzTOucvH0anE0/6Zw8H3h3YwpP8YrsssosVm55s+cf+jSqkDJkdTkSuKT163dDC9rr+T/K+Ws+rdT7jvgdt/ewd/E5qajzllq/6FM/5ZAYrFjKMtK2Kz2FG6Kfi4vI6DTXruvdABFEg9koqsSEZmUSbjB49Hf+AnVmxeT1Tvfrxwy2KeOPkl7ip/VHYBs0zCyFYHw66Iw90tigjtnUgtCrigyzMn5wmqqkUV0fMuoJ0hkUgIDu54WWrNWu5J28jbVSIHwlFEGzG1a4dcAJtNT1HRO6hUwURG3tL+4NvtDk5tL0Uml9LNfy77T8fy+XRvrHIpd+Tv4NkTL6D38kFb5I4xMwuPIc4z59DuPbh59ReoFAo0Xl4IfQW42oGkkwhTTVELOz/KYq63jbe6P0a/KjvnHJfRIvXCYQ7gxyvNFFgqCc8XZ4HBcglegx9GMDSiHtwDd8Uhng7YgsQjCJhAXZMHa1c7EByHmbggmR5DQgiM8mT2kq7dTP9/wkODHmJN9hpu63PbRddxS/In9PHBSNQy8j97mNI21ePlJ5dzY88b/9LxvRhA97Pv4PAwEjHv4h1BZks9x1NnYrbUkJz8JqEhswHQjBhBzYsvUbFkCU1rfyD6s88ACPbuhu5cAzecymfw6QZUdjgU4oXOSwyaK/oEsM4wmxHH28ocah94rOR3n7dEIqGP58X9Z6RSKXPnziU/P5/k+D7s+3k5cq/D9Oz7GD7eAy+63XnMHxrNqdJmkkK98PP4fZ05P+efIdsghTA3agOcieuCIJCWdj3N2uOEhlzJnEeW4u6lbLcXWLNmDU1NTTByBMuWLcNSXk7D+++jGT8BeUh3FIFuTs/YhRAEgd27d1NeXs6MGTOcg5MucOu0FAaFy1GlF+GTupcB99yCJkQMCGcF+SAIAg6dFamnAolEgkKt5pqnX8ZuMePhKwZqAQETKCv/EoLOMWTEDFQqlVOQdx5NFhvzMgvo7+3BYB8N3nIZO0/spNovlqfTiwhWKljbL55Y9/8ZLazzkEglTFzQk+9eSGXHp2eYed94Xj+Vjv3Iz+we0I/xo7vOGv6dEAQBh930j9c6uRT+URwUm8WCIOvIoFQ5bCzNq+COyEAejXVNkZ/XCpBKpWg0GnKPHACgNCudkaF+NAlTeWN0PasPlbH2YCtP94lmwJRoDOm1NK/Pp3FNNroDFU77NBpLL3mOLS0tfPfdd2zatAm73Y7eqkdraeHDoB/JccvDX/E8fH8Dx5r1mOyuCrgAlVVrKS37hLz8lygr+6z988JTdRzbWMjhdfmobfHMvuVRrG019dP+4szHJ1GKx4gRuPfv57LftJZWemaWEX+ykGy9UeQpXPDizD5cib7RjLRMR7Bdzv7W6aw2jaE5twmheTvp+UcIrxpJZnI0vwxsINczA5OhBqkmiDc1X6Pf/iSSuhzY+hgAukYTgkPMwFQXuIpLnYcgCOxuaCG9xbV+fyFOalt5Jq+CvNYuuCNtsDkEVpbUsLq0tq3V1xWtaTVotxXjMLmq5YrExHvgi5nQWt/l9lNUA3jn3BCGN3ed7TIaK8jNXUaj9SBSlZzuM95jiUci10RMYO+1ezGba8jJfYbq6k1dbr/zbA3zPj7KnpyO2rrDZMPWLF5367EapOVeyHODsRa0drkPjE0YdOcwW8SySknJ+x37MpuxVosqrYYjHTo99Z+d5uz+UkI3V6PKMpCqgt7VVgK0NjwdOsayC7VvEri3lSh7znY5bGZdJs8cfob02vT2z5qbT1BQ8CYmc7XTut9UNbC8uNrpeejWrRuTJk3iyA+FZO/oRvo3i9jz0xtdXmKjqZFvc76lTCcq9w6J9efQY+P5+KZBbaKFVqqqfkKrPdXl9lSmM+PnufTR5RAj6Lk53Ln06nAYadaKZbCq6vWExIreRw6LHUNGLW5K50xezfMv0PDxJ5Rcfz2175yiZOkh8k7U0Ko1Y7A7yDeY2tWgQczAHDhwgKKiIt55x7lT7UJYrVpSj0+iRXcjXpN0RH30NuqkJEbv3MLEc2eQSCRotxZT9dIxKh4/CIC+qZFP7lvI+3feyPFN4uTKwyOekSMOMWF8wSWNGFeW1HKgWc87pbXckFnIvTs286XJjYNGKQ9EB7M7pQc9/8Pk2IvB3UvJpFuSKc9t4uS2Eu575F5avUI59OFyauuafnsHfxEtLRl4ef335Tf+N+MflUERbJZ2rQKz2U6qxMzlgaEuOh3nkZSUxNPPPM3RyqPUmGoYd/PtnPp1M4NmXIHFYmGKXUqO7hqyQit56JbLaNlSTGORFrdOXQJyf+eXT3Lya9TnfEiAJFr0uLigPTUtLY2cHLHjIDw8nIEDB7Jy/EpKWkoYVXACVdox3hv8Es+eEs2/qsf1czlvr0628r5+wzv+DvFAKpPgsAvE9PIn2sudlUlRaK02bi47BIHP4D78PqJkXf8sTmo7Bv+dDS0kdfFiSRoRRll2I6FqDStLBjHNMgq7VMaG6MnkK+dhbniLogAr+UF9IQh8fCsY6Pck9fpovj9yP+5SPc8ovkJImIIE6NYngDFzE5BIJSSPvLj424baZhadFWfhH/WMYUaQz0XXvfNsCWUmCx+U13V5/wC21mt5sVAktZ5rNbEiybnkYGsw0vSDyCXQ7Ssn4qUL0rSF+0SCLMBXV8CdB1yOUf38c7Tu20/Dhx+66EIAFBa+RXXNBsorvmL0qBMowvuz4Kq17ctzct+komINFRVfo/FMQuPhTOZ99uczlDUaOZTfIBolGqxUvXkSodWKZngYbn0C0B+sBIeAKroLUajMH2D9bfgA3a5/Ehtmp84OmacnEe+tpv7IEYIXLAAgtbAePUa8tBaiG45jl4dQ2xzP3KVD+LGgmZg+3lgbvqBl+ZfUhY8i4L65SPxcy1TPH32enMYc1uetJ+umLARBICPzNmy2FopLVrdnH0/rDCzJEQOLH6obOTI02Wk/Snc37GYvQMBePM2FVwXwzOFn2Fu2F4Csm5yFKAHKyr8gP1902h4w4Dt8fVKcVxDsRJmr2Z52B6TcBopOvwVDI7Ki/SR3f5YmXTrRYXejP1qJKsYb3cEKDCdqmEgCwo3j8TptQ7utGGX37tCpGzLDYKf8Y7Ez7YdbwshtNdFH48b2FJFj5uvrS0RERHsG5ULYrHYOrc3HbLSRMlvA3BbglZZ9QkzMIpq+XkPta68BEPnB+1hKfZy21zXUYdKLhOez+3eTMrNrxd6uIGkrnl8T4kt12RkKlG40uQehkUgIUCpQ/AXbgL8DkYl+DJoaQ+qmQsK6+3D9o0+w7qklrH7hRZ5e/tpvZqP+CiyWOry9+/3H9v9/Af+oAMVhtYBczKA0Gay0usMr8eGXrH1+ceYL3jr5FgBbrtjCNSmiA2lrayve9mZSFI3YtQrMZ5swnGgj70kkBC8ZiEQpRe7jHKC4CRoit3wCVgPsfw/uS3NaHh8fz/79+3E4HO1qmmMjx4oLe94EM1ey+eQ5aMsU1LTWUG+sp2dAz/Z9qGR9aD75LU1VJgb3i2n/PCBCw4LXRiKRSlC1GbNdfV49N/K3zcuuC/Wj0GgmWCnnni5KYgCCvQar/nMalQH0Hv8gvdeu54xnMnexDrlUIMyiRtWs42fBgSCRMthLfBEHaEqQCVI+s0/lay8vNLZfOYWYiu01poOk6nDYADtS6cVTwr/1yuulcaPMZOnSxfo84txVqKQSzA6ByQGug7fUQ4HMS4m9xYJmWBcqyeEDITAR6nJgyktklWu55oMjGK12Up+cQJCnGlVsHK379l/0HLy8+1Jds0E8ntS1pOHtPYCKClE3RaV0Ta9P6xXKB/sLGdJN/I6bG40IrWKHTH1qFbEzurF1bg4mm4k7VENd06mlYlZEAsT6zHBS1j2PdGCXwQCrVtF3yBUs3icSRafoDpBgOo0duOyOV/AOdMM7UAxodR/vp2XLFgCU3XvhPdM1QBkYPJCcxhzc5OI2EokEd/c4WlpOoVZ1BKrBKgV+ChmNVjvXe3mh3VqMspsXbj3Ea562qA8Hf8gjqfZ5gps3w2tPw6NFzvdZeWnFTrmsg5PRZTo+fCBMfgmMDTD+Kedl382D0sOEAqHLtDT9lEfrMTGLWuphw8vShLciAOmanzHZBmKWSPC67BrirpqDtbaZ1iN5qKTd4YxIOi8xit08mZ3c0eVyOQsXLuTbnG+5+djNhBeHs3XO1vblJacb2k0hTXof+s9ZhtlUSUyM+Mx37gaTBwbi2yMa/dEq3PuJmb2QuAQmLrwLQ4uWlBmuwYld34p+9y7cU1JcyNqPxYYxJiGQc5X5lFl9mNBnONl6I28V1/BwbhmvFlUx2NuDWUG+TA3w/q8ELCnTY6g418SOT85w7ZOD6X7VQkp/WMVHH37FHXfe9Ns7+BMwGEpwc4v67RX/4fhHBSiC1YK0LYNistgJdncnTO1cY7aYbJScbiA8wRd3LyUme0cZwCp0tD96eHhw0003UVtbS79+/ZBobUg95DhabWiGhTob+P0BRERE8PTTT19ynfeTo/m5TssYbylXbJyJzqpjTMQY3p3wLiC+kKoLRLG4w+vzmbKwQ/1X7aHocp+F6XUYWiwkjwhF2oV3hk6XTWbWImZIpAxO2XzRoC5r9zaaqippqqpEuF7KomQb0oRHoEVOXroXyd6lhDCQZOEGpNjIb+6DVhFEVWUCl1sz0bYGcSLxZ2wXlFXMlnpaWrLIzX0as7nSiQsBMDvIB2+5DH+lnL6X4CcAfNwrhgqThUj1xfkFSRo30of3RILoFXIhpGo5wQ8NQjDZkXl1sR8Pf7j7GLZ6I+bSalLTD4smihIJu7JrmTs4iqBHHsbn6qsu2iUUGXEjwUHTkMt9kEo7zuG863JoyGwC/Mcik2mclp/H49OSePSyxHZzO52bjOcwEIGU7Up4s/Iob6eJmhVptWl8OuVT5x2MfggQIGpYl8EJQHNzc/vfuqwOv5gqXw0JbV3yRp3zuXkMH0b96tUAuA/p2i7h0ZRHubPPnXirOlpPB/Rfg8lUjrt7B0k8UKng6NBkjHYHyi3F6I5Vw14IeTQFua8auVzK2Ot7wMq2DJWx0eVYzwx7hstjL6dXQC+XZQBhYdfi7h6LWh2Km1sXRqY1Z2D7E21/n4Xrv+tYJtidVpV2Ulo9fuYdbIKVCEUUtrqzxHsUEJ5wLW49/cGhp+zWmQhWK/EDBhO3+DXCuvvQV7BwuFnPgnDXDr7txduBNtPMTgiO8ULjq0LfZGbg1G6ERzjzt7wvn46qezzNQJHDQUKgGt/ZHbYCEomEvpNcCf/nUb1sGS0//wxAYvZZl3dDrbaW02VFXDlEJPkmadz4qFcMOa1G1lY3cbRZz+1niumpUbMqOZpEj//Zko9UJmXyrT35/oXj7PriLFfddRmvZJxCu2cdh/v3Y/iQvn/7MVtb8wgM/N/pA/S/Cf+sAMVmQeomtgkKVgcB7q4Dy7Y315OfnYqAnZveWMTCXguJL7MT6R9HrLdz90x0dDTR5zs9AhSELh1Kg97M56cqGCUTSArtmJnZBYHHz5WT1mLg0/lbiTKUQ8Jlf+o6otxU3BUVRE1rDa02kTuQ3dBRIojq6U9ovDeNVa0Mv7Jr/5LOaKjQ8+v7Ymo7+3AVVz82yGWdmppNmExiKr2x8QBBQV2fe88xEyg4mYq7pxdeEWG8FLALvwY5weUjaNTbGazuxXWCmsiz17MlKZs90kmkZBQjBRae+h5p/zEc6H8XGoWGtJo0BgQPwG43k5p6OTVnkvHtXglA1smnCZ0+u/24EomE8f6/z7tCJpEQ5fbbpDzfLgKTzpAqZXAJDQ273kL1m0do3raUkGAfVii92H/9YwzXtLB19Qp6T5hCeI9Lax8oLxBX++JwEU9vPovgpeD0knHI7UokEugyE23WI83fAdEjQRNItL8Hc+b1oaTBwJ4RMTSYqvFSetFiaeGK+C5k1L3C4PLllzy/CRMm4OPjQ3R0NF41MtTrclEg8FFYOsqmW5BI3ck9pqNvJ/sY90GDSDxzGqTSiwa6EokEH7WP02cymQoPD1fDUS+5DC+5DH2ndnGpW8d3Z7cbaZ50J955GcgHuRKSlTIlw8KGXfQaJRIJvr6XIE3K1SCVi63/niHOy65dI7ait7WZe02KRhnrzdbXP8LWNuEJ8IzHoenGwaZ9LHlJzHBaazrUqOUOCzEpYoZsJCpG+roaQwLc3e9uVmesZmq3qU6fa3zV3PjScBAu7mAtiYnh0zffxGKxoNFoeOihh7pcr8ttLyETb7db2JZxinkjXQOcRA83nooTg5FTLQYeyClldlo+6/rHu/BSBEFAZ3cgCAJectnf3vGj8VUz4eYkflmVScauMu5/fAmv3X03O1a9QWKP1fj5dH3P/wysVi1y+b8+O78H/6gABZsVWZtdt9Qm4K52HVxKczbgsIpTv8/vX8L8W68j4qFVCEDzC634XHUVACWZ6ZRnZ9H/shm4e/sA4ovs2Z+z2ZxRCeRQ+NI0JBLIz3+ZirKf2cXjVEgiGdkqoXSsq2LlH0WwRzAfTfqI4pZipwHG3UvJlQ9duluhsbICQXDgHx6Jyl2OXCnFZnEQcqGUdxtCQq6gvmEPMqkb/v5jL7pfRUQADTf3ICqoP8ikGG1G8m0yzvmeZpRlFneYbqdUN4b9RyVU5fswo3IT47KyCeytRXrr6/jOnUt67re8kir64bw/8X2GBPfFZmuhqWAsdosnMpWOsv1VTP7rt/A/C4eAYLGQntSN/LZy3Ysze/DDUw9RX1bC2QPHGTjzaWJ6BxD9G6aDIL6kX9qSg0QAidbK5E07uezMMQAWL16Mt/cFQleb7oUz68W/25R+p/UOxeYQuCajgMPNeh4f+g23hvujUf45DQp3d3dGjWpT4IyCqweFUdVaxXTVN2TuqMRispEyPQbBanciVEu6MBb9q9AMC0Pdww+Zp9LJh+nM2Qepq9sGHjAh9F2X7QpOHqPo1ElSZl6Jd1CIy/KWuloyd20lbuAQQrv3cFmOfxwsOgxmHURcENxrAqHPNe0eVuHh4dx2222c9IpHbS4gSe5Ld68BpDfuwS88sj2AUAQHEbP2B6yVVeQne/F96qv0CezDlJgpSDtp5rxZVM1bJdUsjQ1jUdQgPg25IAt2/h6cOcOOHTsYMGAAY8Y4G+81tloorWvp9IkAxYcgcghchI/WGSHPPI1mwnjc+/d3CRyiPfMZGT/5N7kc/b3c2dQ/njnpBdx+upjliZFUWawIgsh329Ooo8EqktH9FDLG+nmxMCKAAV5di0X+GcT0DqDfpCiO/FRAaLwPcx56nF+ee5h3X3iZpa+98LfxUZq1Jwjwv7ip47/owD+qiwepFIddTLkKEhDsrt0ZPn4dM+vg/vVYDR0dGA6DWPe1WSxseO05jq7/nvdud9a6CPJ0npkbDAWUln2CnRpe4wEAVsVHcPyXIhc1SQCaSmDVUHghGFoqXRYb7Q7uzy7lmvR8Gq02BocO5poe1ziJygGY8pup+zCT1lOu6og1hfl8vmQRny9ZRNqWjWh81cx7dihznx7CqGsTXNYH0GgSGDpkKykpPyEIcs6ePSu2Rl6ADzM/5Lvc73j0wKOU68v5/LLPeXb4s5y8aQ/vTwnBU9pKD89fiY2zEaIMYVyWmPlpbu6J37x5SKRSNIqOwVKj1CCXaxjQ/xsGzVAjM8zGXt2Xq5d23ZFxKZjz8rC16WP8VQiCwHsZ7/Hg3gepN3bdpaMz2tHcmoL34I4BQSqVEtlTZO7LfSZzel8FP7+bgc3SUQrQmrU4BLEj5asjxSQs/ZUXfxFl+Tu3vQbpOu5/XVfXdUF54TxqLVYON4sz9JeLmy4anJw+fZoPPvjAxcG8Y4X1og/NmQ3tH0kkEsx2Mzdvv5nPVK+TMisa3YYCKp46TN1HmU6bf3W0hJGv7mbtiTKnzw0tFr5ZdpRVd+6mMr8ZEFWMsw/upaYw3+U0thdv591T76Kz6JD7qZ1NIgGbTdf1+QMOh511K19ibfb3LH/k5i7X2fXZ+xz76Qe+WfqgU/dMZ5TWBnCuKqK94+xCnD59GoCKCrH8suyVWUTPvRfzgBQ0N8aSkuDB0C17afikQwFWnZiIfPRQbttxB19nf80j+x9h7pabnc7h3dJa7AI8W+D8rjhdoaVa21Ge3r9/P1qtlj17dosdZm0wWGxMXr6f2e+ncs53MNdeey2L3TfB59PgeX+atxRR/tgBmn8pvOg9lLq54TVpkpP68nn4uEmQWhqp/2Y0NT+MxVF5kU4owFsh593kKMrNFmaeyueOMyXcebaE49pW5of5835yNB/0jOamsABOtbQy7WQeS/PKOa0zcKRZT3qLAeNFOht/L4bOiiUwypPtH58moVsMETNuxKMsk8+/WPvbG/8OOBxWJEiRSP5ZQ++fxT8qgyL18MamFQcTo7ccocq1JfWKp5dz+u0NSH3PETZ6GKEDLsfLuy9SN3eaIsLJOXyY/v364RMSSn1ZCSHxzgP6zRPiaAp34+qoAKRSCW5uUeh04Xh6VpCTM4KH9y1FM/MNUg+IL5SbXx2BRyeRKc5thbq2cs3R92Dy807739PYwvfVYh39lqwiNgzoWoZdu7UIa7kec6EWj/7OhFaTXo/QNgA2VoovTI2v2kmzxXimHt3ecjQjwnDv57z9rl27OHLkCACPPvoobp0E3foG9eW7XLEGn27U8EZJM1eHDEMtV0OvOTjyjiFRqLhs9htMstmpkRow5Z0jcuVKsFlgy0PMbMgnatxyAv0TifAU+Rne3v3oP6of/UeBvWUgEvUf8zpp2badija/o5jvv8Ot71+rK+c157E6XeRRbC/Z7tL9UZxVzy+rxAF51pI5JEoaCQkJQaVSMX7BHQy/Zh5fvbUZSxtdQJr+JQxewJrsNe3Zo7T5aXx8sAiLzcFHB4p4cnoym+4Zya7Cemq9ZEz2UvHTiRBS/H2Ij+8o5bW0tJCZmUnCsGUEJc7AGDqYtEOHiI6OJiIigjC1kse7hXKwWceHFZ/AsjGQNBOu/crpGnbs2IFWq2XdunX07t0bF2xfCi0VsPYm6NnRAr7u3Dqy6sX7cbDiID1yxe/KfEGb+BvbctEarTz8YyZXdxI2qy7U0lQtPptH1hcw55GBnPxlQ7uK8bwX32p/7moNtTy4T+ws+iDzgy67cLxjX6aiYgsjo11TbhKJlJODjZz1EJ+pZa5XiV9YBIUnU7tYIqKuTMfmlRkA5KXWMP3ujt+W2W6m2dTMpEmTOHDgACkpYgeQXC5j/mW9gF4IgkBO272vff0N/G+9tX17uVSOr9qXaoPYeZOuszAmNZf9Q0RxwsUxwawoqWEiSm74+BhLL0/iXI2e+74VA4ENd4+gX6QPKSkp/PLLz9yp2QkvrCA34SoOjH6F2T5eNBssAOwtMfNpjx7wc4dSb+tRsZNNf6ACn+l/ziemcsODROWJ96emZgrBI5dD/y7UlxHLPgcGJ1JnsRHfpo3iKZc5ib3NChJdlz8ur+PFwio+Lu+YIMglMDXAh/uig+j9G1y0riCTt/FRXjzOnq+ymXfbbF7MysC27VvS+vdhQL+/Jkff1HQUX9+ueVf/whX/qADFJyoWY+o2DEYTHt29sJ9opE5nItCzY7Bz9/dh8HM3txMRATzHjcNms/HFq69itVrZvn07T770FvqGeid1VYCn8yv5tVnLN81aqsb2RSpVYjQsIv1ULhKLGQ2N+AR2PDgK1QWp7qSZ4ozU2AQjF7tcQ4q3B93dVeQZzDzRhXZL+3X0DkRbrkce5Eo4i+rdl8sfeBTB4aDH8NFdbq/9tRhbvZHG73JdApSLzSIBLo+9nJFhI9EoNUw9WUCpycKbxTU83C0Uc4WDuvTrAVBWb6Fh5aNIfPxoWfoVjRkWBnQ/iDTtCyRA/821cO8Jl/23Hj5M6S3iCzx2y5bfVFI9D1tNx0vXWl2D21/kvUVoIuju2528pjyeGvqUy3JdQ8fsVVdvIWm4cyCp9tAwaZyS7ds2kCQ9i6QqGVjAqdqOGabVbuXOMXG8vCWbm4fHABDoqeK6vuJvbmleOR8r/UAHxXYHVkMOVmsTv/5aTF5eHjuBu2+5maNHMznZJkf/yCOP4O7uzv0xwdxPMOwUdS3IdtVS6du3L/v372/XA3JBn2vh4FuQ2CHyVnDyGKoPTjDBJ5TjA1pJCUlBNseMIb0Wz7HOBNMbh0Wzcnc+C0c6f4dRyX4kDgtB12hmym1id5pC1RHEyzr5SWkUGkI8QqhurWZW3CwuhEMQmHNaS7VlMNTUUT3O+XmVSCR07zOYswUbu75GYNT1NxPYpx6p0oDdrm/3ujoPhUrW3r5/XnwNxO/vqk1XUdxSzOToybx5y5uYCwo4N2oU9rp64nfvQhEmShwE3nMPjV98QfCSRWLnj8MOV32KQunOulnrOFB2kAfO1aNT9UZr6Pht3RcdzIJgP3ovEwmyl604wFOXd7RaN+jNGI2l+PjuYvG9U/F+dyUAPc79yJjQe9nq08wDAd6Ya43MRMm3335Lc+sUklW1NM6YjvxMASmlPfCZ/Pues65Q3ZpCFCKJVtkqB0srhpMnqXrqadz69SP0hedFe402RLmpfpMjJpVIuD0yiMsCvKk0WwlUymmxOTjWrOfLygYmnzhHircHI3013BDq79IMcSl4Bbgxfn4iWz88zZkDldz35CO8de8iNq14lfiVq/H6E4EPtAmzOYz/CrP9AUiES402/wW0tLTg7e2NVqvFy+vvJRKlpWez5+WHCbvyDoaOHcfG51Ix9vHhsdtda6cXwuFwsHLlSpqamvD19eX+ttn4hXg2v4L3ysR0e9XYvkgkEhwOBzqdDqVMitpDTKc3VRvw9FO7Bih/IzoHWX8ULTtLaNlZiipBdPrtDKvVSm5uLmFhYfj5XVwC/KvKeh7OLWd6oDef9OqG8UwDDV+JpQpH6ylad7xHbUA/TvcSiYvd+/sw2XEv1JyG+RsgzrVOW//Bh9QtF4mboa+8jM/s2S7XvPjIHvSmA8z2M3NZr8eRyz0RLBaa1q5FER6O59ixv3n9BruDZfkVGB0OXkuIxK2LziZBELALduRddNDYbQ5O769A46si7oIMVqcdQPo3gCD6+UgklLaU8tmZzxgVPorxUeMveY6P5JbxZaXYOXM2xZv041OoKvPlSNkV+FkMhDdUYRIU9J5zFfv3i+3Mjz/+eLvHEwBZP8KxD8RgOPHinRq/F2uff5LS0+Js+cHvf/7L+zsPQRAozcrAOygYnxDnwNxgNdBiaSHEw5U/IggCKUfPUm4SCald6d6Y7WaOVR2jT0AfF2IuQEtLJsdPXMGp9KGoUwfj4RbOzOcm4h/ZsW5zjQGLyUZQJz0ZrVnL6O9Ht5frsm7KouHTz9o1RwLuuYfAey5o7z/2IfwqqkYz/F6Y/EL7osNNeg406VgQHkCQqiNIEwSBWz4/zp7cOu4aG8cDExNYe7KMyJYMetalku9/Cr1C1Faa4Pc8tYfW827L1XzdrzsGtRRJi4XY440MtEmJ8M/E0KpHJ9exPVIMeqQSKRk3Zrjcl9+Dhob9yGyDSNucSb3XQWQRVmYNfZjqBx7kxJlzZCT25I7nniDE/9JWAn8ENofA99WN7GvSsauhBbsg8GBMCFcE+xKolKP6nVyS/d/mcvZQFVc9NpCS+jJ2vvIEtm79efLlZ/7UeWm16ajV4Re1Ifm/gL97/P5HBSgAzyy6B4lRx2MffMQ3WwoxbqtEMj2cu2Z0QX67AEajkcbGRsLCuhZ2A3HGlttqopubCnUXg9p/E4IgUFOYj3dwCG4aTwRBwCbYUEi7bj3+TxzfmFmHRC4Daxl177yDY+R0tmcEYDXbnczTLga7Tkf9qtUooiLxu/56l+UnDh3hbnM1LyF2IcglbowZd/oPn+tPNU3twm/nA6y/G61Hj1G/ahXes2fhM6dr8StDRh0Oow2PwSEuHRg1331P/SuvoLzqasKXzGXzN9fyRt49zFbV4F+dz5i2oMTz8ceQTJyIv79/u7/Tfwp5qYfZ9t7bRPXuy4zFj/+P+qtcDFVmCxktRsb5e/7uwakzrNZmNu2dz6M7b2O6RM3DiDPg8BdHIPmNZ/ylzA3srDjFUwNvZ1xQOLa6OiqXLgUgYsUKF78r6vNF/oe+Bu5NEwm4vxMOh9DeUl5cl8X3r21CVd2CIFiIun4zPmqB0SPO8dED+xAEyIxWsnGoBkVWI7JKkV/37bwEtBUF9Ozfk1sP3EpVaxULey/k/gFdT8h+Cw0N+5B69KNCV85NW+eDIBDj3Y2Y8lv4qdyOw1OJeWgwxWN6ourC0PKvQm+z83pRNR+Ui5NGhUTCAC93bgoPYHaQj4tPUGfYrHbWvXYSm8XB1Y8P4quv19K8Yw0hsxYy7/rZf/hc6up2/J83Bfw3QPmLOHQknUMrnsbSbQCPv/gUb3+YgSKzGeOcCO4fH/+HX2D6ffsov+delDHRdFu/Hklb+tnusCORSJwY9+fR1GrBTSlDfRF/jfNfyZ99uR/Iq2Px9+n0Cvfmk5tSkLW9tI5vWsf+NaL0/c3vfczCA4vIb87nySFPcl3idX/qWH8HLG1S8Z0N/gCKMuo48EMe8QOCGD7nt9ulATL2ZLLa9iPjZdsIoJ7uBXqibqv57Q0vQKnRzIy0PGosNjYP6E6K91/rFnA4HC5dAMXXXocxQ5yZdqUkaynTUbsqHQBVnDeBtznLYhdMnYalqKh9+xmbN3BjuoxhrT5YKtMwp4qy9AeuTuD25y9ewvhvwN72G5f9Lwhgfg/OVDQwfeVRnseNcYjPeNhzw8VWc0T/q9Kyz/DwiCc4aCqtWpGIOiDzHFqbSFa+mGrxfwJP7H8MzzWeKJtFrlhRaCv3PrWaBL9EfnozjaqCJhKGBCCbEUtZmZZnv88k0FPF/kfGoZLL4MxPsPZmbKF9kd+29yJ97BeHxWRDoZLRYNKyuaoIf3SszHgXq91Gr4De5BXEo7PakQD2BE802rXc3mMRVpMdTVujgr3VCgLINArMLZVU1G8GWgkLuxa1OtzlmHabHpm8a8J3ndmGzm6n2WYnV2+ixGQmWq1kdrBvl9nR8zDprWTtrcAvzJ3YfoHs3bYDaUsdieOmEhz0+7M+5RYpYwIj6eV7EWf0/yP4u8fvfxQHBWDEsH7kn7uR+i2fs/Kt97l/8Z28t+Ik8g3lXG40MCY5iF4aNxRSCUFKxW8OTM0bNiBYrZjz8rHrdMj9/ChrKWPelnk0mZv4cuqX9A/q377+7pwabvlc5Fbsf3gcUf7O9UyDoYgTJ6/Bam1kcMrPeHo61//tOh0li+7FnHGK6B/X4t7Dtevm66Ml1Ost7M2tw2i1o1GJX7O+qUOkqnTPFoq0eSCT8OKxF50ClIYKPZvfScdstHHTyyNcxN0sFXqaNuSjDNfgMzPOZWZvq6+n6klxlhi+YrnLLNGu11P39jvIgwLxX7jQJTA5j7RtJegaTJzaUeoSoKSlpXH48GFGjRpF306E195jenP/MRkZdW6odPlEXfF4l/suLi5m7dq1+Pr6smDBAmQXtL1Guak41SbU1lWgKAh2cs8tQ6s9Ra+e7+Dh0UEgbGjYh16fS0TEDUgkar788kuKi4sZP348o0d3cH68pk3FmJGBMq7rWbLUQ4FEIUWwOlDGeLss97/1FmpeeRXf664FQOkfwU6/lTwXlcadPldxKlKKXgX22CZuWBZLnTkKz3s24hcu7uvw4cNs376d5ORkrrnmGpf95+Q+TUXFGqKjFxEfd3FdDIfDSk7OkzRrT9K3zwd4eFw6mCwzWZh+8hy1Fhtr+8Yxyu/Pa0zobXZqc5upK9DSZ1wEbp6uXIO8pjx2le5iVtwsQjWuvK1as5Ufa5qY6O9Fgodrhkl/4CABBw+wdPLlfFygxdQqY8iQCCI6aeCUln1GUdEK5Ho7px2PUFdcSmtNd65JGcJHvjDF34vs7GyCgoLw979IS3nxIbCZIH6Cy6LsqhbO1eiY1jsUxUUGVJHjYEEmUzE8YiSrE1YxOk2NwmbldLcW6itqSPBLZMZ9CRw/MRujqZiYtbMIXfEre0eOJHzparQbCjD7qPCsXoMEkFddurRzqvYU6bXpXJVwFZ5K8Xs8c6CCvWtyARj68mCyT8v54bCOYQm38PSsaIJ3SdmVV8HPEiu1AzMoK1uOw+JB016xwUiTrGLqnETqv96Np2wd8vBg3Go+AGDPQH889QrMwR/hLZfRy9MdQRDQ1tbg6R+A7BKaLJ1xoFHHnWdLqG9UsLF/PB5tqtLNVhu5rSasgkC4SklMtJJ+rTXs/OwsiaFxjLquN6vuv5vKcxu54d13cXf7fRnJ7fVaevm6PsP/4tL4xwUoADfddBXLa2uwn/iVr9cEc8e9V/DdGye4fK+Wb5QS3lV2JJWejw9nYUTARbMZfvNvxJKfj7pvX2RtOhQZ9Rk0mcUW0DXZa5wClKzyDr2Bs1UtLgFKc/NxrFYxkCiv+JqkxBedlrcePYb5hKh9kXXDfQw5vpULMX9oDMdLmugV4Y1bpyzN8Kvn0WKykJelJW1zA1+nSvlo5SSeHv4E1joDDoMNVbQXBWm1tGpFZn/hqToXDxz9wQqsZTqsZTo8R0cg93N+SLWbf0bf5iXS8MmnLnV27bp1NH0ldi1IJBL8Fy5sX+Zw2MjOfoSGxv3EpqymuhBiuij77Ny5E4PBwE8//eQUoEilEnJjw1hsmgqesNzqy1yXreHUqVO0trbS2tpKc3Nzl4PGpdK/Ot0ZKiq+AeBY6lTGjxNfyGZzHekZCwEHBYVvkDLoJMXFxQDs3r3bKUBp6T+d9OsT6THElTsBcLCuhS3J7izoH0lEous9UPeZgMdlIThQI9gcvNknjhl5onXC+6E/Qqg4kPVsrsedBqJVDez9JZOxt4u6JcfSjnEw+CAbWzfSv6k/3X07iLyC4KCy8nsASkreu2SA0qLLbHfoPn5iDmPHXHpQy2gxUGsRs2ary2pdAhSbzcamTZsoLy9n7ty5BAYGYrebKC37BDd1BCEhIhn2UJOOOemiJ8+iA82c2FLM3e+78nYW711MSUsJq9JXddnl81BuGdsbWniuoJLS7nEowjXtz7tgt1N+770IJhMjvviSG86cQSoBZVtGwWS1k1ejJ8AtFmmjjcda/LnG8wv69KknEIgu+5ZnZqdwcP8+vt+7F4D7778fX98LbM7LT4qlHYDBd8C019oXaY1Wrlh9CJPVwbJNZ0hbOpHqZc+i37ePiHfexq1vXwTBQdqpeTQ3pxIRPp/Leyxj8p1DsB//jPUYefBYBNHfyKiPP436WhlGUzEAup9+RQm0HjzILe8foaXOgGeSL62e03jPq4CgpCku3kXnYbKZuH377ZjsJt46+Vb7ve0sn1BnsLA7S8xgHjlnJ8k/iTpdFsNQMExQIJ96I5sLfBmqGcX+NLGzsfhcFelHW0mWfYdG/gvUwEeGm0mw7INd8K17Amsmid/7mzWFxCrMHP5hDVJFAlc8+jgxvX+b4zHKz5Mf+8Ux7WQeLxVWMdHfi3dKazjS7GyaGapSMD/Mn6Shwez7NperH09h4t0PcfCtp3n7leU8/mzXE6DO0NnsuP8vK/f//4J/ZIACcP+Di3jpiTpqtnzJjpAg5tw7lB9fPcEDR01MXtwP1DLeKa3hqfwKfq5rZkF4AEN8PAhVOc/Q3Af0J3bzZqfPJkRN4Noe1+IQHDw+2PkHfPOIGPRmK/FBGi7r5TowBQVNpak5FQSBhO6ukvfqwYNpCuyJpqmQs0k3MaSLa6v0lFI5IpBKoMBobp8VqtzdKdepcbMlYVJDakoUG3doeCRehvHDNLALuPULpMeEaMqym5ApJCQMdvV4cesXiOFULVJPRZcy75rRo2j45BPs9fX4XOmqUNq5xddjlHMXkb41h+oasRzRyFzufr+giyuEgQMHcuDAAQYOdBWk6/wy0FxEEGzgwIGUlJQQqNGgyDqNY8hgVz7AJeDhkYCPz2Cam1Pp3WtV++cymTtKpR8WSz0B/uPx9PRk4sSJFBUVMXWqs8Ln8V+KaKxs5chPBQyYEu1yjAe+T6fZYOWHjEqKX3FtkW1NqwGbgK3eiF1vJcbHl7ndb2V95reMydQgAYwqB6ObLRij9bRI4xk6t0NILLB/IDX54uDx0L6H2Di7owwkkUiJj3+MysrviY97BJvNRmpqKq2trQwdOhRPz46gwlOTjK/PUJqaj9Kn9+rfvHeTAry4LSIAs0PguXjXVH1FRQWZmWKL9meffcYjjzxCRcUaCgtFTywBgdCQ2aR1cq4uC5DTu4uONYBwTTglLSUXPZ/gToTT2nfTkfmpCX2kzRBQKkWdnIwxLQ23vn2RWs3YLBaUXuJkZP4nxzheLA7IP0y+Hn3rz1TKDZwvxtWMD0X+e/xlOpdQ5M7PlEwqQSGTYrI68HVXYikspPmHHwCxTJiUk43d3kpz83EAyiu+okePZSg9AmHsI1yns1C1PRUQsFbqCbAE0cP/RhoxYxzVA4V5DZWT+mGU7SVT0heL3og1agB9fD+jckwvDK15eHjEIZE4P0tyqRx/N38q9BXE+3RkzQbPiMUiheie/tSp5bwwqydrjpWyaGwc6KrxGyvBEN8NdbIfCg93busjkuStc2Hbz3swu9VQaEmk98CRkPELACulw2lRT2bK5KNslFzZfqz6Lb9SL9Rj1sThrbicX1ZlURZn4qBDzecLUogPunh2LknjxoMxwbxQWMUnFfUM8nJneWIk/TzdUUmllBjNbKnXsrKkBnmkwF3n5Pz64WmufXwQGaOvwLB/HT/+tI2rrphyya/2SLOeCb9T5fpfOOMfx0HpDJPZwsuLH0bdVMaEh5+je1g31r12koAIDTPu7YdMIWVPQwtvFddwvEWMrF/vEcH8sEsTOc/DbjchkUiRSsUXjsNiwVJUjKp7vFNb3R9Fc62BuhId3foFIO+Cx7KiuJpXikTdhHX94hjRSRr72KETpK6pR+pQclhh4JCHhO03Dcb9yxwQQNnNm6A7/vMW4A6jEYlc3s7Zaf/cYSHr9L3U1++kf7+v8OvkxvxHcFzbiqdc+pu+HqW33ELrYbFO3xUP5M/Aam3GbKlzcRe+EKf3V7Dvm1wiEn0ZfG0ICoWCgE5iV3d+cZxd2bUkRXiz6Z6RLttbynQ0rTuHItwT3znd20ttX7y1kdrUL5BJ1HQfOpTYfj3pOca1bGCwGliybwlpNWl8Pe1rEny7FukD2pVQz2PZsmUAmExVlJV9hp/fSPz9u25ZvxCCQ6CxqhWfYHdkctfnwGKx8M0331BcXMxNN91Et27dqK/fQ0ammGkbnLIJT8+etNjsvF5URbhCwbVqDb4hXZdjLXYL+c359PDtgUzq+rzYHAJpzXr83slEbRI7biJeGdVxvjYbtoYGrGoVnz90N8YWLSOvu5EhV1zDsJd3UdUmiJa5pB+jdv1CTchgooy51KkjWBjdjSfjwrDb7eTm5hIUFOT0HTuh/CTYzRDt+psvbTBQWK9ndPdAJA475ffdj373biJWvYvnBPG7raj8nqbGw8TFPeTiGWQu1GKo1KGJbEDxxXhw2MiIWM3BE6FI5CYSrngYicSE0aYiL3w7e8xGnowNxaP8SWpqxW6s8w7SndFiaaFcV06SX1J71qmgTs/MlQdptdi555pePDSgLfjW18HKAWBugf43wKxVLvs7c+YMFouFvn37IpVKydx8gueP1XHcJn4vt07+gVUSUX5hyTer8aorplWlpCEsnnDjTAAqww6zPuAYSiGQk4s+RC6Viw0BDsGlPNZqt/N2cQ3JGjdmBfl0mSmvt9j4pqqBNRkVXL+tmdjBwUyfn8RzDzyMsq6Yq15cTkJs18Z/dkFgV0MLkwP+GeWdf0myfzMam3W8s+R+ZBYD17/wBm42DZtWpBPbP5BJC5LbX/qVJgsvFVaxsbaZH/rFMczn0tLgOt1Zjp+YjSDYSRn0E15efSi5eQHa48d597oFaGfM5t3kKAKVv6ODRhDEh1r9+37kRruDrysb6O6hYqyf6z20mGzoDVY2nK2mV7g3g7v5YS5sxq6z4tY74KJ+Ha6nJbC/SU9gvYnQM814DAlFGfbbkumCw8HZA3tw8/QidkDKb67fFcyWetJ2ppK2SU1wuAdXPdVVLum3UXTNtZjaZut/JECxCwLFRjPd3FQXLQUVnKqlMq+ZAVOicfNStq/32tYcvjpawotX9GZm3zAKCgr4qq3kNWfOHHr37o2t0UTtqlM4Wm34L+jZ7s77e2DUWzi1vZSQbt7E9r94uttgsfHh/kIifd2ZM/DS5L3jx4/zyy+/tP//fIBy5uyDVFdvAGD0qDQUit/+je77NpfT+0SFurvfH4/dZmftjnSsPv7MGxLd3olyIUymSuRyTxcdkr8L5tIWLMUteKSEOHn5nEdNYT5fP/4AAN5BwSxc+QlnK1vYk1vLVQMjkCql9D5ytn39j3rGcHmg9/+KTqaaohZ+fFXkvl3l/zCnFUEcYAju+ijUxjA08mYCJSXEDfuc5NmpcG4bxIziWPZC9HrxmjoHKOYiLXUfZ4FDIOyZYUg78ci2nq7izq/FUmNKShhr57SVuBsKxAAFwC/Oxcn9YjBZ7RwuqEfa9Cb70uqpa43maMAOVlcWsC93MoLEgCG2J3KrBovcwI2WEVyR+AAAH0/+mAFBKVz9/hHSy5p5cFIC90649MThYig1mnly7WmGHtbSb34C8d3d+HDJPdjcvFn63rsouuC/HGrS0cfTHc9LOKf/X8LfPX7/4wtjfj6e3LTsBUDCV88+hcpfwsQFyeQdr+Hopg555zC1krcSIxns7cHcjAI+q6jHdhFZa4CWlgyENqnx2tpfAbCUlJDRPZlNIyewr0nHladcZbsBtmRVMXvVIX7JFFUc+XkxvBIFy7tQ87wAWrOW7Pp0bgn36zI4AbFbxs/PjVtGdmNwN3HgU8X64N438HcHJwBfVjaw7NB2thx6gczsTGrfubiMdWdkH9rH1tXL+enVZzm17We0BqvTcv2+fdStfBe7VnuRPUBGxkLO7RWX11S00nryj3fqAES88zYhy56h+4H9f2i7+7NLGXEsh7C9XfMtLCYb2z46Q+bucj5/9BBRO9NJOXwak9XO6r0F6Ey2drVPk6lDeEuvFyXorZV6HK0iT6P1SNVFz8NqtboI57lplESPD+fG7VnEPPYLWeVd38evj5awYmceD67N4KdT5V2uc37fgwYN4oYbbmD27Nk8/nhH2dJT07P9b6nUmYtkaLHQXNtJrTn1I1g5iKbC4vaPSo1mdty+hB4P3ULMomt5ZJ0YLB7Kr2fJD+lklDW3r6tWh/2p4ESwWHAYXFWjL4QqygvP0RFdBifnFZcnLryLoXOu46Y3xVJWcpgXd4+LJ9hLTYBKwYLwANxlUj7v1Y0ZF5mR/yHsfBZe7QZn/1oXVl1pB/ctK/pxjkqGIHE4CKneg0HWgNEcRKkphcjTZtHDad2t8GYCPXu+RUz0XQwdst1pf8bT9WAXQBAzea1Hj1Fyw3ya161jbHwAS5IjeHBkHHeM7iCP50sFpiX2Z3L3nuhv29n+eV5THpd9N4beX/Tu0jZCrZAxPjGYMUNf5bIZ96DzP8oNDRae53mGX9abyVorMpsdm0JPf89YzOM9UEgUKKQKegX0ok5nJr3td/TmjnN/+h5Gual4/ZreFMeqSf0ujyargsE334NXSznr17tyAQH0dsc/Jjj5T+Afy0HpjG5RYUx76Cm2v7qUd55YyuMr3mT4nHgOr8vH009Nr9FinVwplbKmTyxL8yp4/Fw5bxfXMM7fk0QPNQsjAp1aJkNCZtFqKEAu96ZbjEgSjVi5kqH79pOglHHOYuf57q71d4AXf8mmotnI3d+kMb3PdChqGzy1pZe8DkEQmLdlHiUtJXgoPDh6/VGXdfLy8tixYwfJycmMvYhg2cb8jTx35Dku63YZL4580WX5d6mlfHGkhJ79gvn47NPEGcuBz6n03AaArrGezW++jK6hjhteeRsPn06EwLMbcTvTwdn5IqOZDXu2MzHchxckcvT12Vh+eBWJA+pXrSIp+2yXJL3y4wL6xkO4KS4nWqVGekGnERnfw+b7IHkWXPmhy/ZabRqZWXehUPiQcvVGZBdoMNQaalm8ZzEluhLWzVhHsIczFyen1cSlIFfK8Alzp6m8lSrPMh5vE2zVJcWxYEQMnx0q5sXZvcg9cgAPDw1z5sxBpVKRkCCWWdSJfniOieAcdoJHdZ3dyMrKYt06ccdPPPEESmUHd+F4USPlTaK2xeq9+bx3g8jVOa+Fo/H1o3twx2CfGOIazDZvKUK/vxxVvA+BC3s7yekDVOmruP34D1To3flu+hqne9jabGbNsqNYTXb6T4oSu7B2PAPWVsbZ7mFu2Af0GxLKsJ9OcmPQ5ShmXM7YHc8xNFYkKz+6LpPyJiPr0yq65N90RuvRoxjTM/C9fi6yC2ZttsZGimZfga22lpBnn8X32o5uJXurFePpetTdfUWit7YCTn4OCVOcTP8aKyv44qG7cdhtDJw+m7E3LuRC7C/fj9lu5qXuE3k5wfX70umyadaeIDRkdpdBltlcR33DbgL8x3cIeVlaaTr4MQbcCP/hRlimpUxXRmFzISPCRyCXyjnUpCNTZ+TGMH885DJ+OFHGa1tzmD80hvsndufnwp/ZXrydO5MX0WtiKN8UfcX7ys0kBEYz/ag/KSdO0OjTQlb/2wiQl3FWCMSYXcjYtvOSSSOIi3uQZquNE9pWBni5IwHSrPn4KO0EdgtBFetDyVMrMKanYzhxghpzL2SHRQFByZCOrqlNqesoM4uf76zYz+z42QBYN93D1qJ0cpUKthZt5YbkGzhWdQyNQkPPgI4AWCKRMCRqAENuFSd8D5xfMO8hYpqbsVgsBAWJoohpdGRnPHzgyWlJpJU2OansXhRmHRibwSfSZVGIWsniO/rz1bPHWP9tNk88NILD30eQvW8nXHO507rZeiM9uugK+xe/H//4DMp5DOyXTL8Fi/FoLOH1p56nz/hweo+LYP+3uRRndkT1apmUNxIj2TkogRlB3qQ2t/JMfiXb651nqTKZOwndlxLb7d52Yyi3Xj3pfvci9o/oTfW4fhfNcFzRXwxcJia1DYoz3oa+c2Hh7i7XPw+H4KDJJBL2Wq2tXa5z4MABamtr2dvWUdAV1mSvweKwsKnAVf4cxAAqu6qFH3/NQxPYMWiFPSl6TOQfP0pVfi76pkZSN3Qy2bJbYd1CulWvZX63NG59+yN2GcSXcVCFAVu5BVVrFIKnyBvRDEyEZ31gmTdYOwICu81G8X4HJl0xTY3vMvrJwbglXlACSf1AbNnM/L7La6iq/gmLpY7W1jz0etfSzr7yfWTWZ6I1a3k/832X5e8kRXF7iA/Lm/IpSj/pslwqgSunlfLNuP3UaDpmbfoqA8/M6EnxK9MZYC/m5xWvsu7FpxBqKtqDEwCJXEr6kABmqPUMPp7DtnrXLEhBQUfK/Xzm5TwmJQczLyqQm1RePDW2O1lZWWzYsIFjv25mzROL+fqWuYTdOodfNzzE4SHBdHdzJTsbM0VxK3ObYd+FOFFzggq9mFn49PQXztvqrVhNYgaxLKetvX3QArTNKk7v8WVM0Zv033sTB3IWcT/FPCA7QOCcZ5nZ059TpU1Y2jgH3QLELjd9k5mizHrsNmczOLu+lbLbbqduxQrODXYt81lKSrDVioaZDR995LSsaX0ezT/lU/2aSC5l66Ow/zX4eAI4Oo5jNRlx2MVslra22uUY6bXp3L3rbpbsXcKyI8vE87LbycrKoqqqCofDRtqp6zl3bhn79vfr8l6ePnM/OTlPcPBQh09Li8nOKuktfMT1rA9fitFm5Lqfr+Oe3fcwYe0EtFYb12UU8mxBJXEHxA6a9/YWUK+3sHznORyCg6cPPc2esj1cu+0aekz15XDQZpBAvnsZVov4jvBrPscJnxdZnrSKLUI3hL46NqaE8Yp8Ia+++ioOQWDqyXNcnpZHn0NnaGxqYnfWcexWG5LcVnY9fpBffG7DotCgjI9DuIhh346MSOymEDCHMDKwQxgzoVE0i+xhsXJVwlXsLt3Nwu0Lue6X69iY//syRz4+Pu3BSVe4bXQs790wkDCf3yDCm/Xw7mBY0Yvqb67iWNUxl1XCvN3wGR2CZ0ErNY0GgnoPwq2hCH2rc5au1GQh5jck+//FpfFvBqUTpk4eRUNdPTWbPmHF66tY/Mi9tDaZ2fbxaa54cICTjHUvT3d6ebrzXLzArFP5vFVcw0R/bxR/oERyMTw0pQcPTemkbNttlPjvNyCTyvh0yqecqDnBjLgZXa7Tr18/SktLiYrqmtQFsKDXAl5JfYXpsV3PXK8eFMmnh4qYNySK4Blrof4cBHXotcQPGsqZvbvQNdQxePbVHRtK5RAxGEoOEtRnNISE8vZ1UradruHW3qFINxZg01oxvhBPctyzuGe8DecrKPpq8I0Rr1Mup/f4KWTu2srwq+ehCOzCG2P4vbDlYeh5pesyICz0GpqajqJSheDp6TqrGhsxlk2Bm6jQV3BX37tclidr3Bh3bDtpv25iPbBg+fv4hXWaOZccRr1pIZ8o3XkqojeVsjBmD72Mbskd7cwqt47zVnu6zqrrLB2lrzKTxWX56NGjsdlsdOvWzcVywF0pIyrXiM1sZ/0LqTSGHcbRNuh6AgEtBhz14mzW+Okaqo9d6UQMBfCZEYv+UCWa0V1ncMZHjWd67HR0Fh3PDn/WaVlAhIbJC3ti0FroNVYMuIXJz5P/1WbWx3anQhnHIO0vmHwaiFE+glTqwMv8Lm/fNJyjQ++jVide++4HxyI4BNa9dgJ9kyh+1rmVWKpSIg8Oxlpejlv//lwIt379CFyyhOLCc9SGBOBTWkxAVEzbthek3v275iYEW/O55YpYakMuI2H0Za7HkLshlUhxCA6C3cVJxZEjR9i5Uyxj3HbbQuRyb2y2FuTyrjk6MpnrTNtqtWJziO+TesG73VoBwGgzopJK8VfIqTab8TPqWb4jmyv7h/PWznPMHxqNVCJlTMQY9ufvZoZlMIomK/dGRXGuoQSvwgnUe9dyYEAYHkMaiTCauXeVgNKeQWuFHct8OynDN3D20BRqs1dzu+EnNjKbo9aRTEgrpXr05TSWWXjirJlEKeQC0pU/ETc2giiznfAevoTEenNCYkVwCBzdWEhCRSDF9Q+gk8Kp4ws4dPAyRo4cSfzEp3jg1FsYlB786LA4lSwdgoPys6cREIhM/u0S95+BuUhL88YCrG51NK55AQxKsge9Sl/jLyy0LuQOxyjuvnElkk4dgeOGhrNjayWHTteS2KcX6Yc2cDa3kMEDRFuQOosVf8W/w+tfxT+eJNsV3n7zPWypv+A7ZT7zb7iKjctP0VJv5KpHBzmZgZ1HmraVmafyuC7En1d7RPzt6ph7GlowOwSmBHj9ryDc/SU4HGDWgpvvb69bmwPbn4SooTDqoYvqMfy3kLFjCzs/FrkIiz5ag7tXp8Gn7hy8PwLsFpj0PIy4r8t9VOXnonRzxz/cNZ1sFwR+qG7EVy7nssA/3gWwYfkpKnKbCIrxpDXkDMXFxSQnJ9PD1xMfDw2SL77GUqZFlXwrErnaJUCxOwQ+P1yMWiHl+sFRTr89QRAwG1rbvaV+D4y6Ft65ez7vhy5kmOws3yhfQnz7yJBI7BgcYazK70Fx/Dh+scTj56Hk8NxB6A9XcSCrgcq2oOVCrRO7Xo+tqgplfPxFn493brwKq1nMwk2LuZ2oqwbj3i8Ic2EzykhPpO4KkYxelysGwoq2gKG1Ad6IhzY/HZZ1zefJb8rH6rCS5C8G6kePHmXrVpGXsGjRIvz8lOj1efj4DHRp1wVRiba5+Tg+PinIO6mhFhQUoNPp6NOnD1KplILmAs41nWNi1EQUMgU5DU088PMMlkRUIZPA04cf5dDS2532/fPbr5F7WCwTJ97WxLJDt1NnDGD61AjWOQRGWk6y9Nwp3L4pxNFURJ23jDtvDWaKTyVjVvvhE2ZAfU0DfmVKNDqBAUN/bH8W07IFMlrt2N0U2CYX8+zRZYyLGsdbY8WW8N0NLfRsoZ2ga5bCB5fZuT1/I7oWsZsp9IpQ3k1/F4DHBj/G9YnXc6jyEBqFhoBGBd8/8ygAQ+dcx4hrbujy/v8ZCIJAbm4uHnv0SEqMFJ96G/+SMwDkxE2jMnI67w+7n1u22bkmbDrhb7zevq3d4eDde/biPjmMlHgpu55fQu/bH2fyhBEAbKvXMtn//8D7+g/iXyXZ/wHcu/gOXl5aR8O2r/k1OJDpd41k3Wsn2bwygzkPD0StceY7DPD24PUekTyYU0aZycKz3cOc2lsFQcDsELr05nEIAoeb9Qzw8uhSzOdUfR3v7t/MCe+eXBUeypuJUZhMVRSXvI+vz2CCgy9dn/9fB6n09wUnAEGJcMO6/+z5/AX0nTSNqF590fj6o7jQ4yYwQfRScVjB7+I29aHxF/eAkkkkzA29iOro78DM+/th1Fnw8FbhcAzEarU6GwUOH4VgtWMuakEZ5ZrB2ZJVxfM/ix0chXWtTvX7TW++RP7xI4T1SGbuc685bScIAqbsRmTeKpTh4mBrLiyi9KsvkJgFNDY9xZIQ0lvC2FUhqujOi53CEZk7rQlllE4eR9Gw3kgkEqrfOomt1kCKDFoXJDt1JRVl1LHn6xyievoy4aaelxwMonr3o+DEUQJUkXhKfGlal4dHSgjqzt1REon4m+sMpQd4hYO2zMm1+ULE+zrzcwYPHkxAQAC+vr7tIoC+voMvur1criEgwNUcM+4CleE4nzjifDo+U0m1dNPUI2u79Lk91oPxGth0jxhczf3GKXB2C36NOmMzAJubLeClYFReTwIqEmAMHMpfwZO9R6EqjGCc4iP6p2SzqSIJ2d7JLJslSh6OzjJQFtCE1vE6UzwayLpLLC/N2/IaNsHGjpId7ceTAL4h7viGetBU1crwnr7cM7A7JwM9OHXqFJMnT0EZpGRHyQ7MdjMz42YikUgYGS621Vc0dnRFiXtzxtrqRoKUCpI1auQSCQUGMzUWKw4BtDY7MW5KRvhouvxtnDlzhh9//JEYexAT6Y0sIQV99Tnkdgc1QUMJSLDxyhoZsaU2NHPHOG1rsTuQCqCUy3DYxPLfeSsLS1um8p8WnPwn8G+A0gWkUikPL3ucl5Y8zNmvVhIY5M/l9/Zl3Wsn2fJeJjMf6OeiPzI31J8QpYJHzpUzNjWXRA81ce4qHAKc1RspNVno6+nOGD9PeniokQJ5BhObapvJM5jp4+nGt33i8Fc6fyXxP9/GuuI9ALxyo+hIWlT0DpVVP1BR8TVeXn1wc4vEajZRnHmK8IQk3L19/idu0/9pOBwOTp06hbu7O0lJSRddzze0a6Iz0CXJ7o/CZDOhkqku+rIzm2tRKHyRdmX4KIECqZ14uwN3mdQ5OGmD4BBQJ/hiFwTmZRSyq7GFt3pEcn2YP9GdVI7Pd3sBlDUaeLMyGGXgJCbn7nLZp+FEDU3r8gAIWNATdQ8/qpctw5GaSl8/T6pDPqfHSC1pxzv0QL4zV9DsJQZ5K/v3aL9edaIv+loD8gA3F8Xdo3vLaZFXYg+6l917dAwauA5v735d3qdZDz3Jxrd2414qPrfq5N9u2y4xmrk2o5C6Xh+yo4cvsVFd/w5+zaqipsXEvKHR7TobUqnUhVR8KdhsNuQXkWnfeLKc9/YUcMf4OK4Y4Fxui9HVMKDOQL6biiilgytGPQ65WyC7jYi+5WHG3vgB8SnDCIqJRenuwU2O96gsq6VfnZ5vZ85lfI0FkGCwtlDi54XcGMZ46Slmyo6CB1wWeo4fA25pP6aPwcGY/W58lqJHo+kIoMc7+pNvPENvi3P5WOkmZ+7Tg2nQW/DzUGIyFWMpuIceiVoCg0bi7RXPg6M/J73FAFLncm14YjKzl76EXCohuqdziafeYuPe7Es3DgDEuCmZGejDE3HOitjn73exrJama3wY1vtRdA234ubpRf82wrltgSiDIL9A+Te1sBGpAFHhGkrLxKxLRIRICBYQLRj+xV/HvwHKRaBUKnjgxedZsWQxO1e8RMDzbzD9rj5sWH6KnZ9lM2VhT5eW3HH+Xhwcksj2+hb2NrZQZrIgl0iY4O9Fdw81qc16vq5soMEqRtxecinj/LxYHBPCXWdL+LGmkTsinYlenpbm9r8fixUfAG/vgVRWiUqSCoX44Oz8eDVn94sk2r9qc39i83r2ff0pIfEJzHvxLZflRZn1bFmdiVQq4ba3R7sEazU1NXz++ecYjUYeeOABfHx8nJZrzVpu234b2Y3ZfDblMwaFDOJSeHx9Ft+mlvLC7F7cMNRVcbUrfH20hKUbThMb6MHuB8e6LN/V0MK8TLGNvGB0bzwuUJzNyMhgc5tC8PTp00lJcdZrMRttbFx+irpSHTPu60tUG7dEEBxknb6burrtJCW+TFjYNTSty6P1eDWe4yPxnhzTvo+GTz6h9vU38Bg5kqiPnQmcAD8X/szjB8SW3uPzjqOWO2dpysvXkHtOVBseOyYLmcz55f5sQSUflIlE16qxfV2CnKaN+bQeqULmp8Z6bx92NYqtqEtyy4g50sSwK+NIvSMGWfZP+AfEAGKA8N3xUkqVoaCEKyar2bU7juDgGfTquULccefjtP2t6tEDQ2oqUY06HoiYQ2VdBvX907E3ygg+a+EcZ3Br0iAz6gmmhvz8bwkMnITPtAFoJkSSWn8cWqsJ8RDPocps4em+Uvr2rqI3OgDKyj/H23uFy32sra2lWF/M1sBf8KvpQV5zd+66IRm71UHGnjI0PiriBgahazGwKvdt9FY9Tw19in2NBoqNFjzyreQfyEXmKKEpwkR+w0km3noXPiGh5FbrWLRG7Bj5/kQ5v97vXCbT63NJT1+AxdrEyBGHUCpdA6POGjNPP/20k6mk3epgxQ+nKcLO4h8ymObnSeuJGjRDQlFGeiJpLOC2ai2mRinSofcgBI2iRFpGtH88NOTDqAepqdtMWcsKaLyRKM0t3FJ5BMOpE4CEyQPm4WYVvyMdDmTaOmYaf8UvZASrG39kqs+rnJHMYWZLABVnW2gxNNGjykyjfzEPuC/guqvuaD9X92M1XHMuAnDABRXNTw4W8cIvIhn94L0GbDaxVFZRvgZZQi+uyyjA7BB4tqDSyVRx1+kMbl0jtsAffDSOCN+O33hLWxDwUvdwglUK7AJEuymJVCuRIqpIH9XqeaWwindKa7klIpCQTqrBiYmJLFiwAJVKRUiI+Lvy9HcW0bswMDmPnWfrCAGG9Ajgoz35WKQKuncTJyRpLQYm/UOE2f7T+DdAuQR8vD1Z8OzzfPH4g6x57inuemMFk2/pya8fZnF4vYoRV7mS6lRSKTOCfJgR5OOybEG4+ONvtdmxA54yafug8UFZLaf1RteTuOZLyNkCyTPbPwoLu4rAwAnIZJ5IHKDdvBl55cW1Mjojoy4DP5UfkV4Xn91nHxJ9dKrzu9YMKDwldkU4HAL6RjM+wc4DY0FBAUajeC0ZGRmMGeOcHs2qzyK7UXxZvXD0BTbM3nDRc9GbbXybKs6Slm44/bsDlM0Zoq9HYV3X3Uydu2IKDWZ6e4rXYDDbqNdb0GjE0oRGp0Pz4kuUBgYSsepdpG0zq/pSHXWl4sC456scbnpZrD2bzTXU1YmaEdk5jxMafDWtJ8TOD93uMqcARbtBvO7Wgwe7PMcjlUfa/240NRKmcZ4Btugy2/+22fQuAUp5F8TazjDnNQNgbzQRqVZyd1QQG8/Vctl+LaeaGhk8oxtBO++FipOQ+no7/2JycghfHC5Bb7bR3Wcz2KCmZnN7gOI+MAiZpwLBTUp5bS6hzQkEP/E4fjfMQxERgbXehOWjjcgEKfHusRyboEJd0ZNax+eMmTyN3HPP0dCwh5LSD5kwvoDPcj/nnVPvALDjqh2EeIRQbrKiEwRSGcppSX+a8eYDRQC9LrjG8vJyPv74Y/aH7KfOrQ567uDo7BNIpBLO7q/gyHqxE+rohgIyheNsS/wOgDpDHa+Oe58dDS2oLUeQjX2DfCDy+KOUZJ7ik/tv48Hvf8bHXYFGJUdvtjE8zh9D2ilatv6K73XXoYqNpbZuO2aLqNFTW/srERHznM7P5hDYlXWm/f+CwwF1OeAdDmpvNr59iqu0SgwSge1uFprW5WGrM2I4USNyhnpfDa11qFVeOAbcxKST5zijN5Ey/Ds2DxTfT0VH7sZkKiMv/0W8A+azfs4DdB+YSf9IH648WsATci96oOZ9czGJQLDVhFoXiwBsaX6Cb4PqmX3uXaILrRgiu6Pz9QablZoN+zloVTGoWyLWykqGzJzD/u++JKRfCqmpqfTr16/9uk4Ud/jz+AdMJUyXjkQiJaH7U1iREKCQU2G20tfTjWfzK6iz2HjMV076ym3c31rN8NBUzmYHEjG8o0wW3GaC6iWXMT3Qp8vf+EhfT77s40bvQ6f5qaaJRVHOE8Do6N/3PumMg006iou0hPgocXdX0FxejMMzBFmb3onOZsfrX+2TvwX/Bii/geiIUGY8/DS/vvwkq5Y+yWMr3mLUNd058H0env5q+oz742l8jy5+vCN9PVlT2YDR7nC2//aJgqF3uqyvUPiCSUvD4unU7mkgAgh7ZAmRM2df9Lhbi7fy8L6HAfhk8icMDu26Jj7quhs5vO5beo+f3OXyfhOj0DWaCenmhXegK2m4T58+lJSUIJPJGD7cVbY7JSSFmXEzKdIWtZPpLgaNSs7d4+L4/ng5L1/pnOIVBIHCljJq7V709/Zy4vgsnpTAG9tymdE37MJdAnB7ZCClRgt9PN3oqRGvoWF7MbN3Z1OGg1HdA3j1tjtpeGM15OTQmpODbvsOvC8XOT8h8d4kjwyjoULPlNs6hkWVKoToqNupq99FctKrSGQSvKfF0nqiGp/pzlyUwPvvp27Vanyvu46ucEefO7A5bAwNHeoSnADExi5GLtPg6zcclcq1xfLF2HBiMpoYVmyksSoH/+udSxQ+V8RjOFGDx9BQJBIJT8WFMbdZzramRnyC3ZHKpRCYKAYondA30ofTz4r+I9VVVgrPvUxkwKz25RKJBHUPP7Z/uJKsXaI2zpJvN6FsGwwUQe40DluHTpJOS/cjRACn165m47BinluwiPz812ho2NO+P6OtI3C3OcTs4yAvd16MD2N5zj7q5eJAfFdqCZfHhyPrlNk8Hyj7m/zFAAXw8BZLXZ1/u/omM4GKKDRmH/SqZm7rcxv+SjlfeZSwP2gb5/up8vzE6xl3s5g5kJhaeHdqIGHRsSSE+pA3fjy2yiqavvyKpJxsQkOuoLHxIFKpgtDQOS7f0ea6Zr4M7MZAg4WExERkJz+FX8VnlIfyaK4RW1fdBQkbnh6PZXcptjoj0jYeXG3DLrLMK/FS9qa3MJ98g9jpdN6aAyAifB55+S8RGjKHFbvO8dmxanwsCuYd+YxrJA56RZ5lc+UoppbUsmlMDf0FGaFnDlETnEKvMx9TNHIKEkG8A4qWRuwabxAEzP6hyJFSdpvopaOMj+O6H3/kjTfewFZQwpYtWxhz7xIAHp2aSKBSYHplGuQWktSnQ19JBexM6UG5yUKjxca1bZnN1BoJ1zYfxIGDg6U+PJj9FAzf1r6dh0yGm1RCY1tG+mLwU8i5LsSPlaU1zAryIUzt2lL/e7G7oYU7zxZzYyt0O+8w3lCBOlrkZ+W2mrp0xP4Xfw7/dvH8TmzfdYj0D1/FFNmbpa89z5H1BaTvKmPqHb2J7ffb7pm/hWKjmZHHsrk3KphHY10t4btEzhaanr+V6pM+AHT7aT3qLvgSVVojN36SSrFlF+rQnwB4a+xbTIqe9JfP+7+J5SeX8+npTwGoi/rKKTX8e+Bw2JBIJO1dFQVvHmdyXS3nq8cfJ3ajYkcqvbNWkR/miWzkcKY/8Bhqze/vXPlvwmG0UfncEbEoToe/TIvuNHpdDsHBM1wE6lx34hBLBf5xIJWRX6snQKPEx73tJX94Jd9lHCTfPYr7xl6HV3SHeeOvq95qLzsu+XaTk//U8ewX+fLsV3RX2wlSe/FiuYWHBz3MjT1vRBAEjMYS1OpwpFIFZruZrUVbSfRLpIdfB6lYa9Yy/PvJ6PwXIkg1SA95U/TcVCepfEEQOHPmDFKplKCYIPzc/JBKOs6jpcGIUiWnMr+Zitwm+k6KROOr6lhn1RAc9TmUhruhmPY2YaGi0JtEIsFqtfLGG29gNotBwbJlyyi75x70O3ch8fGmx5EjTmW1mqIWUn8uIn5gEInDQpBIJKRpW5mWJvJ1vujdjSknX4MjYkcLdx+nWhdMcVY9PUeF4+mnRhAEHAYbsjZhwvSMhe3B3LixuexvNnCoSc9tEYF4SC3cu/tejlcf5+tpX9M3sC9fHinm6Y1nSNLlMLFe3O5MQl/Konqz4s1n24Wxgvo3k1U3kVUzbsK3VcG0ooPU2Iqp85QjNRtwRMZjayuxzN20GYfBgPesmQS+8AJvv/02er2eoKAgkq69gXFtRnm1b7xBw8efABC3bWt7wNoZdRYr007mUWay8EF8BBXPP4dRW0i8ZzOznl8NoR0+YQa7g9j9mbyTFMU1IZfmFNVbbEw5kYuXXMZXfWKJuEiQ0mKzU2w0U2W2IgUi1EokEjHwWF/TxLb6Fib4ejL2ywr6T4wkfkQAH98+F7/L5nPLgmvZXq/9x/judIV/u3j+S5g8YQR1NbdQvfFj3nrlHR587H50jWa2f3KG2Yv7ExL7136UMW4qFkeH8EZxNdFuSq77Pd0b3UbhM3UEysBTKBZ8gfIiZM6DefXk1eqBFAZEBvHQhCGkhPw5D5y/E3a9Bam74g/J63dGblPunz62wVDE8RNXYLPpGDRwLd7eAwi/PI43f3WQE6Dkjit6cnJtPnrPKA4OWoi1dTNkpbPpzRe55pmX//Rx/ychdZPjd30SltIWPMeI5EqbTcfJk9ficJjIyV3K+HE5Tttk7inn0I959B4XwciruotdV4GigNz6tHKWfJ9OvKyOSZ4VJFk8iK/QkR41ii8ThvNuoZTqTmNOjxHXYTb3JWV6gos55s9NFvbqFezVK9hx1S9c59FBgJVIJLi7x7T/XyVTMSt+FhfCW+XN88Me5UT1CaZFLGDQxFgXHx+JREKvXr1wOATOVrXgIRforEnn5e8GDgexiUpi+7maJVaGTyWzLph+rQZCwq512bdcLkfS5ItnSwLbPjrN5Lff5rWNj5JVWYRl9TW8Nv11YmLEazm2qYCy7CZKzzSQNFychAzw9iBtWDIKqQR5o4VD9XPo1c8D774jITCBkEDwkzbRumsz9qlTkWk0SN1kOEwmpGo1ERG3UlGfj87Rj1aLlaaGXczy7Y68Vc1J3WmOV4sidI/tf4xf5/zKjcNiqNJI+TRHTfP2bOoDA9k65kocMpnYJNMWzPrGG3jq6uvI8w1gZtohBvmPJrtZhb5mLwKg7p1CfX09vr6+xP7yM5b6RqptgZj1du68806ampqIiIhgb6Ou/X7JOvE5JOquBdMClQqODU1CQOxiEz54G7vVilzpGlAcaRbFCXtrftuFPEApZ03fWG7ILGT88RzujAxidpAvMW5KHAJktxr5vrqRLysasFxkzt5To2ZVUhQT5W58bSghINKTk6fOIAGSeiVSb7Hh96/2yd+Kf+/mH8C862ezsrYWy5FNfPJpEDcvuJZNb6fzy+pM5jwyEJ+gLgTD/gAWxwRTZbbyQE4Zext13B0VRC+NGxKJpF28yInoqPJEcsOPePyGNMDk5BC2JdVQpzfz/uwp+Hr8+RTn3wX94UqaN4n1//AXRiDpwtX2YrDZbJw8eZJ5fvMI0cTi8Ejh1njX4MxoM+Imd355OcxmWg8cQBtZjs0mvjwrKr7F23sA6h5+zO7RpkZ6cAUTi59hRGI/dFd8z8/LT9JUVcnQ6ddiLtaijP7vaxwIgkBTlQHPADUKpWvZUBAEiGvFq2d4u6O2RCJHJnPD4TC5ON4CZOwqxWEXyNhZJgYobahsNnJmy9fcW3wQubuVpHEFyGQCjqzbuKeqL80ZBwmJMoG9J8gUCILAzs/ysJolVJzL4+73nY+V7JfMetYD4KXsmGkJdjv25mbk/r+vvXpA0AC+OvsVWfVL+Db8W6Dr7Nar23L4YJ9YOih8aVpHICMI8PUVULgXekyDud86bbehMohaBnC0CZZdsE+5XM7tt9/Oxjcz0bVYyT9Zy5TbepF0JpweDjvpgjeff/458+fPJy4ujm59AynLbkKpliEIAhk7fmXXJ6vbjQc3/3CG0rPNpDOUu2ePbjs9gZIbb8JWW0v1U0+TmJlB0XXXYT6bjf+iO8mcdB137xBLQh9l/0KttyiYN6FiAp5WT8YNH0eONofVE1djdwisTyvHRyKg83Hw09SxDCttRmMxEdZcT8z0ZrRnFHiGm5HKoNq+Aa86K4/f+gz+1d6M8RtAH/1svIP/H3vnHR1V9b39z/SZZNJ7T0hCQgst9N6kN0HAggL23sGGoAKK2EWxYBcFFAWU3nuvAdJI771MMn3mvn/ckGSYBGzf9nt51nIZ7jn33Dr37LP3s58diFShpKqqCn9/fyQSCUf21pK07wIA9384iLAw5+fgPWcOmq5dUYaHI2+tojM4FN+USCQtGieCIPBZXhlxrmri/2BIJd5Vw7bucbydXcz7OSW8mVWMTAJ2saQQHnIZT0QGMNTbnWCVAosgUNDA4wrXqBoJtplnxVChb6gbWw+mYkNKl07xHKitZ4TPf4/X//8CbhgofxIPP3YPb5SXUrXjR34P8GfMg4MaNVKmzu2Oxu2vT/5SiYRlcaH08HDljawi1p9Mw0Muw1sho9Jio52rmnVdY/60EJyHi4KVd107U+YKBEHg5cMvs/7yep7v+Ty3tbvtTx1rW3kNz6TmMdTbnffbta5Wa2pWvEww2yiqM+HlokTTMNHuTikhraSO23sEU3juJP6RbRpTek+dOsWWLWI9jptvvpmETglO4392/jM+PPMhapmaE3ecaNxesuR1qteswa4QCF4zHalcTWzM8077c+FnADTVZ9FEBDLnvc+wm6wULz1Bmf48ijA3Ah7u4rSb/lwpNVuzce0VhPvgP89PMlqN5OnyiPFsXXTsCk5vy+HoenHSve+DQU5GSk7OJ2RkvgXAkMHJSKVKZDINPXv8ht6QjZenszR8t5ERHPk1gzYDgnlzawodQzwY0ymI179aS9/ibRTiim/7amQy0WAuiPoVl6Je3Fm7jb4XDsKAcXBsBZILv+Dv/y0FeRJ8Ql2djjM9fjqDwwbjrfZGIWvKrMi9+x70R4+i6d6dyFXfX/d+bc7azOVqsejmluwt3NK2SblYEATqq024eKga6xKBgK4unXzBhwiZG1KrFZecw2JT6man8QMDAyltkMrHYmwScAMOFRzi8T2P49s+gGnWeUT38mbx2gX0N+7H7/cS4kwCW0aPQqMRjeROg0PpMDCk0ThKO3IAgJpSkUTrH+lO7qVKp3OQujQtfKyVlZguiQTzihWf0FayAw/Z3dTYlCSEGNhWFoq5YjDFFjuemHgq+gkMRXl46pWsS8tn7rrzSGQ6AuPexmI3YpR0YcT5ZO5UjKJKvpaTIb+QKTvFb+2kqPVHWTVmFVGeUWTllHFhzWW63RSO2lVLRX4ultpaaJCXv7pual1lBRp3R6+yvbYW/fETCBarg4FiN5upWvUDisAA3EePxlZbS9GLL2GtrCTso+XIrsoCBPiioJx9VTq+6RTV6u9EZ7Tw1rZUvjmSw5A4P1xVctoFuXNPx0BejA7iaHU9+Q2ZlpEaJT08XFFe5ekLaSEUVJ6nQ+OmwNVTSXl2JlZXP6RKBQqJ6T++aPm/hhsGyp+EqJHyHIufnEvKqo/w9/dl/KOd+XnpSTZ9fJ6JT3ZtcTX7RyGRSJge5M2UAC8OVus4W6tHZ7NTYrLwc0kVSzKLeKlN0DV/CE5E2xYgCAJZZ8tRucgJiWtyveqtetZfXg/A68dfdzBQ9CdPUvP773jdehvqOGd3OMDneWWUma2sKa5s0UCxWnWUlGzCbVBXpJogpOHufPHWKcyFer7RGln1ZA9KC/L45MvfkNptHFxlo2uNqHd//yffovXyxsOj6cPn1Uoa4KGCQwAYbY5F/QSrSPaTWiQUFqwBGUS3edpZR2TYAti/DLrd1WxfAbtJjLtbS1qujlu7KxdblYnardl/yUCZs20OSeVJ+Kh92Dt97zX7Vpc2kUdtFrvTe1dX3xQCEwQrIH5s1eog1OqWeU4dBoTQYUAIr29J5tO9ovGzZkI4E079Qp1PO9xCL6F0NzX2P5M0mIvmPMbYteACrOjT2DZBmMoiv2cps5opKgonKMjxmFcXYBTsdgznxGdtONVEzDWZTOTk5BAREeGk5TIqahTbsrdhtlkYFjbSoe34b1mc3JwNwIJlfWkX6Eas8jdOnnycwmOhbL3QFqVmKIOHfU0nl83QcSpUZIh8mwZMmjSJIYMG4vnLDFgcIJZQuGkRABsyNmCymagWcki86wDpRijdkI6uXIO/SZyxB6TrCA5uIjg3D0H1veV2Dq39nrg+A9izZw9V1iomvzSYj0/k89iPZ3hocDTv7Uwn4fE3GV+XTJHUjlmtwuPxxzBcvIh3dwseOas5Jj+KMGUFP/8UxCH7vZRKVBwFXpkdQ/bB3ZzatF58tnPfbzgJC1bBDBI463mWSHUIIVmeACT63kzxqDDKc74CRGJ9gl8C+1enUVdlIvdiBXe82p5v5z6G3WYlru9Aho28hx49/PHwt3D56Bo2vrWXrLNi6nXCB001mso+XE7V96LRGb1zJ8pQccFR/dNPlC5dCiBWnZbK0O0Qxd6KFy12UHAFeDurmGXZxdwf6sfIFvgegiCw6lgub25NodbYUEPJYMFgsbFibwbLtqVyc9cQFozvwLC/4PEoy6vDN8wNiUSCtSwfuV8oh6vq6OP5v8FN+1/CDQPlL0Ahl/Pk4ld558kn2f3BEnxeWca4Rzrz69un2fHFRUbd38kpFv5nIZdKGOzt7lBQsJObhgWXCyk3W3k5OthJ1K3SYuWNzCJWFVXwfFQQD4f7t2rIZJwuY9vnokt28O1xdBggfixcFa7cn3A/GzM2Mr/3fId9Cuc9h6WggOrVa2iX4lxgD2B2qC/ndHoGejsrkwKkpr1CcbFI1B06IY2cC1WYC8XJfrReycpV69CWptKrSiQOGhVNq+8r1xIfH8/DDz+MWq3GrYUaNgDPJD7DN5e+YWK0I3ch8MUXce3dm0L3HWATtSfM5lLk8ijHAWJHiP81g8xVgd/9CViL9bh0a7kwmbZXENW/ZaL5i8TpXJ2YUl1hrLhu3z6TonHzVhMa54n66mrOQEzMc6hVwXj7DHBKQQYoybzM+mWvIZPLuWvZRw5quJ7qpvHkP/5GfNDjnMr+Fp3Mh7piT1Sry0hJ7IPUHo9KH8ou7sdVVkSw8jyHIx9hQM6HpHd/GftpMTU5JyfHyUC5GhKplND33qVu3z68Z89u3L5u3TrS0sSU94ULFzrs08ajDY/Ffcbd35yk24kDHJw3pFErozSnyUvn76ZmXB1UVCVT7iej9KwbUIRZt4rjF28mNVDNlLM3IxHsMOYt6ClmpkilUrzUQEGDF+7wh40Gyh3t7iC9Kp3JHnWUFXyJJ5AU4U7fujspij2Ah0RFwidvUvDsXGp/+42QD97H/aabMKamUr1mDeX+PSgvvgmXTHdOlYjaRZvPF7LTKL6Lx7MqKa41shUorj2IpjKPA2u/R65QYrcYSc7pSVH4J/xQNh+3jfcwUwVbq3+gVA1IzPxa8yt95U3cnsFxAWx6zBd3tYJiczwFugJKDaXk1uSi8PeBAhPe0+OY4daJNPKx2W082vVRAKK6+JG0J5+Qtp7YrDbsdtFQry+uoOLri9T5nqOy27t49YHzX8ZxpQ6tzdyU6q4MaxKZk2ldm21vMuSVUW1QBAWijIrCnJWF7/2Osv1lZgvLsot5ONyfl1pIJhAEgZc3XOS7oznM6BHGY8NiHQoEGi02fjldwBtbkjmbX83q+3rj7/bnsm7K83S07RmAwWjCpb4Mtx4DMQstK4XfwN/DDQPlL8LDXcs9ry3mq3lPsvq1l3nwrfcYeU9HNq84z8Gf0hkwLfYfd/fdH+aPh1zGS+kF/F5WzTAfd9q5qpEAaXoTOxq0PUb5erAos4h0vYmXooPwUzpPXnJl049JoXZceT/S9REe6fqI0z6aLl2wFBQg92t98h3r59mqJgGAXNa0yhAEgZC2XoS190KfVUP4hEgun02iVqVGkMmQ2Gz0GjeeU2fPEto23kEh1+8a52ATBFQusSwduMwpHCZ1ccFj/HiUhq4IWSq8vHrj4uJonBQXb+ByxpuEhNxGVOTDjvsH2bB5loLMcfV/Bdp+IWj7XUNd9jr4eNjHHCw4yNS2U6/b18VdSc9xUa22q1WBxMTMbbU9+eAe6ipFQygvOYk2XZuI0+llzaoj+8QjMSrIkdWAYEGwgqsQy/DthzgzMAgjIjP2edtUDpue5YWYDgyYvYgos5l+mn24u7vTs2frMu/NoR00CO1Vujlm87X1XM7lVTf+nVqsazRQBkxry/m9+bi4FfP29HHIZUrGRc2izuaOxSMXRY0BP703Bt335KS7kB3mQZS2Ckqb5NVNegvHfy/FM+pzOmm3w/CFjW0Jfgn8OvFXcnO/IP3yEgBW9JtJTshyuElOcIclSO0GahsE/zJWPEqdxguvJVZkWfWo+BEGf0TuuVr82ntQX1nEtOG9uHSglsIaI3f0juCt7aIXbI97L/LcR9K2/jK3Va/ntrhzKDnEKJ9PiI36meJ94j179oEoMvbfi0mh59d0JR3b/8ze0wZi1VKyEnsQdFt/ajrEkX68kDbuXbhp3CRcBvgjCAIWQaDeZueF9DKk/o/zdnwYqoaQx8Dpbek/NQZpwyQ8feEb6GuqCdW2pfK7FOr8RY+XRAIqNzOx4e2JGTWTHJcmQ8Trzjtx6dMHRXAwsmaZcNqBA4nesR2pqyvyhqKX0Vucw20AR6rF9Ok7g31a/L5+dzSH747m8PrNnbi1p7MHV62QcVuvcPpE+zD90yM8vOo0a+7r84cXlMY6C3VVJnxD3TiXlIYMO9L4eLq6/T3+4Q20jBsGyt9AWLA/E+YtYPOSF/jopReZ9947DLw1jn0/pOLuo6bL8NY5GH8VM4J8GO7jwbeF5eyqqOVwVR12BMLUSu4J9WNOqC9+SgWriyqYn17AxtIqRvh68HZcGG7N9FciO/ly87PdUapleAc7cwRaQvBbywh4bh6yaxDcrofY2Bfw9R2Km1tHpFI5lsIsEs4dRe7XEbYVMOC52eTl5dEmMhKFXIZS48LwGSILOG33POq+2IjPyGlE3PlKq8d4IiWXn4pFYairU4/NBitbP79AUUYNtzy3EO8g52vPyv6IuspyDuz9HsPA9gTHxuPu7oulpp4jqZOwWAtxc4umdy+xGJxJb0Gplv/lbKTmSPBLIMHPmVPzr0DHwSPIOX8WtdaN8A6dASiuL+bNE29ilcYAEUjtNuRrF6DvMw1XFy019VVEl1QhtUs4NW4mrq4aVgS+jVFqZrTfAt4NiWJiF9FAUyqVjBjReiq7uaieiq8vIFjsBD7bA6mm5c/RlClTSE1NpW3blsOKc/pHUWeyEReoZVi7JsPRzU9D/Ohwzv4oPierzUwa5ymy2BlRfw6XczKq3PM44iKu4H/J68jTL8yEhGmNYxSt/QBb9nbW0YcTgYOY4+Fc2TksbA7u5p5IylzRBHkjEfRoNBEEBU1BEAQ8Z0yndstW7I8FY7acwxAlQ5sl/hbd/TR0GRxEx+TnkJBKTaGBWT6dqFPVMavHIG6XqbEg0GfreRAgzTWGWHcDSqnowZhWsg2/5AIu7YhCG90OQVfPqELxnt//8P088lMWSfJwkqzwkNaKl20tv2/oSplJSxFphOqicOnsz5wL2Wwpr6GrmwtndKJH00ch55XYJmNbYrdhrapE7utLaHyHxu2y+xS48gSXCqpQlZQzZ8Z4NIMfB+B8SRV7Kpo8WfgEgskOpmbbAFw8RKZqQ18bouCZZ7NvloDAW1lFxLqoyNSbyNSbHIaoN9tYfCyLQf3CCIz2dDzu1ZDA7EnxLNmcwhunsunTpmVSttDUHYCyfB0ZgQpSvCQcPplDbWgMA6PC8Vc5LwJv4O/jhoHyN9GlY1sq7nuGk5+8zlsvvcKLSxehqzRy6OfLaL3UxHRvORTwd+CrlPNUZCBPRQa22mdGkA83+XrwbUE5b2QVk6DV8EiE46o/KNoD3f58Cj48g9xHTeCz1049lkgk1/Se/BFIpUp8fAY2/lt/4gRImohonp6eTtL4ADabnrqVv6I5LUN/ai11Ex6hXFJPmNSHun37ce3dq5F4l14vfrgUFhOCIDistArSq8lrICLu+voSk57uxqGfLyPYBQZOb4tMISU09A6SNn2GLs2TLeffxqiC6aFPUi2tYa9cLOjWpasYHkram8/+1WL44YGPBiP7D7p5BZsN46VLqGJjkV5dvLAF+IZHctdbHzlsW5W8qqHY2w5WJgUTkiGGnHQZW/h6Wi2vf2XDVwenBneiQFFKRuJw8uWLQCIhrE0gEyOa3smSrAzObNlIfN+BRHbpztUwnC/DViN6RwyXKnDt7uyVEgQBjUZDYmLrJG9PFyUvj2/vtH3WV8c5kF5OonsoU+Pbo76cRejO7/FvZyds4gt4dfkUc046eSkeVGg19JZqKIwegNaia8wsCjAf4lfEyTi3OKPF4wtmG3Vf14K1Gt2GAtq+8VJjm0QiIWjhQoIWLiT11HZSth3GGnYYV3KJWL0Pu86KvrgIoSwNo70Px88ZKVSIKsg7fv6BHqntOIuVQRIZ+91PMENaRUT1MlLt9VywrkCXMwW5awVzb3uKud99Sv76t0AhZrSt/eFbtOUaXIimP1YsboBUQbxHGWWlWopV/iCXYhMEdjZM5md0ejzlMqqtNm4Lbpq0BbudrOnTMV1KRjtsGGEfLW9sU7XxRIUn/dqsd7o3kwL+QGFQYy0ceAu8oiBxdqvdPsotJU1vYm3n6BZDyKuO5WAtNbBsTlv83a///g/xcefQkQLOnynmhR6teyKb48ypaqqq7Ixt48eFb5NQl1UwPfIP6lbdwJ/GDQPlH8Cwwb0pK72X/HWf8M4b7/PMC0+iqzCy86tLuHgoCY7x/I+cl7dCzhORgRSaLCzPLWVygJcTK91woRwAa4WxpSH+EgRBoKr6KEqFD1pty6teECXmX4zpQvuqM8zOukjA07e32lcmc8G1Xz/sp49ik8CM3bPJ0eWw6DctbS9UAzTyYj5oF84v639BsfEH3vkCnvxhA9KGWjshsZ6EtfemOKOGYbPak3mmjIv7CwAwG62MvKcjYaF3UheaAWmi2/pMbBV31MmpkDTdI2+vNwAobJCLB5Go+p80UEoWL6bqBzFNtjWO0PXQL6QfX1/8GoAgVQeUHbojt6TgP2cyw/zSqX/Xlf5d7ydKpsZkMnHUDDsuZAGQb7Q4jLXvuy/Iu3iei/t2tVgfyqV7AMb0KiQKKS4Jzl45m83GV199RX5+PgMGDGDYsGFOfeyCwNOpefxeWs1XnaLo79U0cZ1tCP2crFXx8xtvkv/EE+hOXUB+VIbfl3cieW8ZCl8LI0PSsA1czPnB4dy1TiTa/jz+Z+K849CMX0jkytc5bdVy2BLtdHwQuTNSjQy7zo68WdkHQRDYlFSERiFjWLsAzm3zojp3INUMZOCv8Rg8tez95BgAFrdXCZd1JQwjHorD6JX1eLj9RKXtKV61VVCq9EJV2ZHZNVXgA66CK2tiJmO2enC0vegBuv3Vd/k29U5MWiPlJ0OpEvyIkhropjQxxdwb1dqPkchjyT+UxLokF+aNjCO0ofjgu/FhbCqrYW5UIPGuaqfwiWAwYEoVDfG6XY4FIgvSqkg/UUKnwaH4hGgxZh9HcuJzVIkzIWogx6vr+KawgjuCfejjqaX+6FEqvvwSrxm34jZ0CJxYCYcaCLwKDXR2VlZellXE29klPBru3yq/7XhWJQmhHk7GiaWwkIqvvsa1X1/cBg92aBvWzp8lm5Ox2OyNhR6vhbI8Hb4hrkilEozFuUh9/no49waujxsGyj+EGdPG8VFxMcZD6/n8c3/unnMb+hoTmxs0UrwC/1gY5V+BeVFB7KioZVlWMe9dlVnjPjIS3Z48h9WrtdyA3WBFGdbyh+DwL5dJPVrM0DvbEdHR2TVaUvIbFy89CUCXzl/i4zPIqQ/A5/ll7KnRs6dtHKNnxBLq0fo9MuktGNu9SvAmNS5hLhSuGYCnzI5qWhmFDwn4v9TkYm3rqqZ9STbpDf+22ayNBopSI2fCY12artVmQaGRYjHY6Ty0iayn7TiZz/XHsbsWEXxxDM+q64mxeTN4QB/cLCX0rv4NysLoNaENSrWMiI6+KNX/2Z+T5Q/UY9qavZX5B+fTN7gv7w99v3F7VXE9h9ddJjA6kDN3nIF6PeUFn6KhCA/zLji7i1faT4JL62HnEtTzy1GrPehecZjHXY/j49Wbe2Mcwx9hHTqRd/E8rUGR9gUB5hUwcCEoOju1GwwG8vPFQnEHDhxo0UDJMZj5sUj0iE09m+EQ0vv49m7svFTCHb0j+O5oDiEzH6NzQmdc+/UTheNm/ooubRcHQ0cyMCSC7JRvG/fNrs0WVWuDEkhJfJnv9mWibJjABEHAZrGz5dMkci9WMvbhBMIe60ZOTgZnNRncZDOjlCnZfqmER344A8CzI+PoFO1BWa4OQSpgidbialXi6qmivtqEuttNcL4MraCms8WEMHAdVpOUPfkrmW43keoaQ7FbPywX1yNrPwFr/gnaq4OQuyWTVdUdvX0vQ85lo9oei7ZNb7qH9MXLBKUqGfP6uLDaXcVSWTjPFZQzpdcADo/zbLzWfF0+J9I+ZFxgT9ppneX4AaSurgQvfYP6w0fwe/xxh7adX12irsrExQOFzHq7F1XfzibUng8X18LCGl5IL+BCnYF1JVUUD+lCyRtLMaWkUL//gGhIBzc9M4K7Or8HNjvv54ip2FfqmbWEklojYV7OXJDS996jduNvVH33HW2PHnFIWw7zcsFiE6iqN/8hr0t5fl0jUVitK8Yl3vm9vYF/DjcMlH8QDz4yh6XlZdTsXsOGAH/GPjCEX946LWqkzEvExf0/I5Dmo5Qzwc+TX0qrsNgFFM24EupoT9TRno3/tlYZKX7vFFgFXBID8J7q6AEx1lk4s110+/++/BwPfzLU6Xh2u7nZ301xYqOxkNTULZSWBpCY2I+Rvu58VSB6cK4ntnRo3WWSD4kT8L3vDWT5sOVk5X6DQr8TAPknjtk6/abPRKFUEZ3YC4WydTn3zTvXU+ieDe4Q2Ea8FqvVimteGr0zuxBZKqNSkYfXYBOP3DUBtUIGS6PAUAmnv8JzYQ1DZras4Gu32akq1uMV5NoiCc9mt5FWlUYbzzaoric5/wcQ+MpCan5dj3bIkFb7rElZg9FmZHfeboftZ3bkkp1UQXZSBVGd/fAOcifwhWch6WdYJ8qTY2gq+IbdCjIFly+/Rs/6NKj7EEmsYwikz5Rb6TZ6AkpNKwTCXa+CzQS/3AMJtzg1a7Va+vcawom9F3DVRVFVXO9k6IdrlEz29+TX0mq+7eToph8Q68eAWD++PJjFq7+LxNfPZo7mpriGMJRfW27OFkjKrIXMJLIGzMBitxDuFs7IyKaU5ceHR1Cu/opyYwmFVZ35ZdUvVBcZ8KoQw1abPjrP3cv7cvv5OdRZ6nj+4PMk3ZWEe7NMKH83Fe17aXhGNwuDXMenP8K5medw71FC7sULaLvPwHdIN6YfPE1JfRc6FA9h7hk9SXaxynU7fS7z/W9mn48bLlnfkNG7F1ptBV06b6a8dgO76hUciYFA3UQ8S7PIzTtEb7/xHA7vzAW1FMwWnkrJJd9kYVNZjYMhtzJpJZuzNrM5azPdAroR5eEc7hAsFso/+hhzVha2qirCVnzc2BYY7cHlk6W4eaupr68n0x5MKPlNz8FLy4U6A94NVc/dhg3DlJKCMqrhONFDYW6W6D1ROKvCamRSdveI59ZzGUw7l8G27m1brGemksswWGzO+3fsRO1Gkags0TiOf6W/SnF9aQiL2UZ1cT1dhoWRnJaN0m4hqhW5hRv4Z3DDQPkHIZVKeeblZ1n89HOkrf4YXz8fxj3SoJHy0TkmPdUNheo/U+VyRpA3n+aXsaqoglnXWIUIFjtYRWpYS2Eflauc+D6BpBwpZsjM+BbHCAq6GbncDZXKDw+Pbo3bzyc9yO5d0RiNeRw6dBL9ED3FQ978Q+ev0TYZd1KZhL7BfUn0bcuFi3os5gpi2j3n0N8nJIzRjzx93XEtFksTA64B+fn5pKdcws/FigB4W6qYM66XaJwAhPcWRb0COzmN1xw7v04m/YS48mvJkHv9+OusSV0DQNJdSdc91+tBERCA7wP3X7PPne3vJLc2l0Fhjl6tyE6+jQagm08zY7HTVPAIA1dfULnBqW8gekjjROLrO4z6+jSnTKgrULlcw3PY6344/AEMeq7VLj7VFtxrRAMwa/tevO4c69Auk0hY0SGSFR1a2luEn1uT8Xf1Krl5JEMj13BfgmNaK8ChwkNszxWFAZ/d/yyx5bEgB5t7BV7KEEbd3wnT6bM8/3UdB9vaOdJfzETpE+3D5scGoJRLiPF3w2g14uXphqFex+jI0dTU1HA+SfQw/fDDD7z08gKyg7woMWkZe+hXXGo9GBAwhXThPJHqfvxesYZqbzUuPpNwzT1O70vnqOoGMSobu3SiMSSRahBsIoflaNlvhJ2+gEvEBJTugUwL9OadnBKUV4Vvegf1Zl36OgACXFrOTrMbjZhzxYVJ3Z49Dm0j5rQncYIKL98opFI5xTe/zZHyfBL7D0cBLIgJ4enIQLQNRoXfo4/g++ADmLDyyblPUGW4MyBiADHdW6+n09ZVzdou0Qw/kcqb2cW8EuMcWmkboOW3c0XY7YLDgsD7zpm4jRqJ3NsbidxxyjufX42/mwoPzfVJrhUFdQgC+IZp2XVUDP927Xp1/ewb+Cdxo1jgvwC1Oj1vP/kESkMVNy9Yhq/Gh1/fOk1wW0/GPNCpMVXv341nUvL4pbSKnzpH072lcIogIGQeoGTlNupTawlaeB8uCdeehP8MTp+5gwP7NVRViR+XdVHr/vDEbLcLFGfW4B3k2qLmx59BvdWGwS7gq5RTW1tLeno6cXFxaBtSH81mM2vXruXy5cvMnjWL8IgIx5i83S56UFyvnc20dskJynJFOf2WDJQHdz7IwYKDQMsGSpXFyse5pXR2c2Gcv+dfvNo/DsEu/KVMJLvd3Cil/3dgteoapfivwHjyFw6vOoGrtIqet3RD0tvZgPgjuFRYi49WScBVBkqZ2cKR6nqGers1TqBXo9xQzr3b7+Vy9WW+G/UdxWeKqaqqYsKECbi6ir+jnNmz0R85CkDspSTk0pbXfnqLnmpTNVqllid2P4FwQaCNoQ0zZszA68hRth9JIdnsym2HvgMgs080+6N7oyy6jGdZGZ56E3Xd+iIvHcztxtvY5eVBdUgMEfVG8oQI5gfeycMHPyGgqoyEvFI+Hq3iRHsTZ2aeQS6VY7TZW9TrMFqNqGSqa0oj6HbvxnDmLN5zZiNvJpCYnr6E3DzRyzZsaAYr88vI1JuYGxWIp0KOYLdTVVyIZ2AQUmnTPf7i3Hf8vHcD45IfAqDDAA8G3+5Mpm6OpZlFfJpfxrm+HRyyEgFOZFdyyydH+GpWD4bEXz85wWC2MeDNPYzuGMhrk65vaFzYX8D+1Wnc9/5A3nvzAwyXjjJ/1Zrr7vf/E24UC/wfgLubC/e9toiV855i7eKXue/Ndxl1X0d+/+g8+9ekM+jWtv8RSeRXYoNJ1xu55VwGH8SHO096p75G8vsTBKogs9CPnGk7/zLZsiUkdPqE0JBTHMor4/305dwScQuCIHD5VCmCXSC2R0Cr90UqlfxpsrHNZqO+vt7hh1JmtjD4eCoVFisvtgni0YgAunfvTlZ5PWvPZjG+czB+biruuKP1AkeW0lLkfn5XO16cMHxWe9JOFNO2Z8vZVgv6LGD95fUMCWs5JPNBTgkr8sS6H1vUbenq/he0Fuw28T/59Q2I1owTW3U1SKXI3N0py9ORtCef2J4B+IerOfrLajz8AugycmyL+7ZYQ6oF1OoucOLEZMBOt64/4uUl6qaou09mqMoOak+IHd7Yv85cx6rkVbTzacfA0IEtjllVVcUvv/yCQqHg1ltvRaFwNmz9lAomXPU7MKZVUXe4EG2/YNSxXvhqfPl14q9NHUY5H8swsi91Z47i5RPiYJxcff0uChdcFC7syt3FiZIT4Ac2fxuhej3ZS5bQFQjuqaJ2ohXtThlqmS/h8mjC/A8zwv0YFSfc0K0tYOvwHgwbPp2u9Tspstfgbu2GsSSAe9M+RmnT4x3nwZEuPXnorikkRCc0nlNrYmJquRrBLiAgtPqs3IYOxW2os6FdV9dUcDK93shL6SLp/MuCcoqHdGHbpx9wca8Yim1Oli4u98AuaQrJ1NWkAaKBYjDkYrMZnUj204O8eTenhKPVdYy4SkU2McKLnlHeLPztIt3CvfBwufZC5o0tydQaLNw7oM01+11BeZ4O7yAX5AoZdYU54BV8/Z1u4G/hhvTdvwghQf5Mee4V5BYDn8x/Ee9IFwbfHsfF/QWc3pbzHzknV5mMHzq3oaubSyPpzAHmJnEuqdxO2MqVf/uYgiBwbnceR369jGDT4O8/iMndp7J3xl5e7vMyBalVbF95kR1fXmLvqtarEws2AWu1yWn7jpwdfHz2Y+qanfsVfP/997zzzjusWLGicVuhyUKFRZS/XlXUpNZ699cnePX3S/RYvPOa11P20UdcHjyElA4duZ7z0TvYld4To1vUWgEIdA3kgc4PiGTMFtChWZXW4L+is2CohuWJsMhPDM38BRhT00gfMJC0nr3Q7drFwbXpnD5RzGN7Unln515O/vYLu75cwekfvnXat9Zcy6QNk0j4NqEhddkRVVVVfPTRRyxZsoTSkiTADkB5hZglYinTU7TsJPk/BmILcDRCvrzwJcvPLufhXQ9zruxci+d+9uxZ8vLyyMzM5Pz5Fsi6FRmwvCe81Rb0TTVwqn/LwJhSSfkXFxAEgYsHCji/Jw97s4Iz9SYrp3IqsdrsHCo4xC2mD5jzpJxPXu6CXRCvw5RbS8ELByl4/iC2Gsd318UWT5i6G15Kbxb1eg1FSAgSFxdsWgHrXTrqRtopXmbBJeJOfooNYIJwCI3KSmh/kQf0gQv0NPdiabY7t56Q0Pn8ZbpSyIj77uOWZR9z1j2BCoWKrWu3opQ5G6c1pcX8vHg+2z55H7vNhimzuulc65o4ZGZzOefO38/5pIew2Ryv4UCljuPVdcTFvUJ4+D107rgWd4OdyIZy0c9FiYZ5RX5u007vJYgeSGBGwlBKq6dyRHYWRcxn+HYX3yG9PosjR0dy7PhoMjLedjhmhFqJq0xKut75WyCRSFg2NYEag4U7vjhGflXL5SjMVjtLNifzzZEc5o9rR7jPHzP8r0jcA8irC9EGR1xnjxv4u7jhQfkXomP7aMoenMvxjxbx9kuv8NKyxegqjRxdn4mbt7rVlfW/Eq4yGYO93Xg/pwSz3e5YHKvXA+DiCz7RRC50VP6srzFRkFpFRCdfVFcJatnqLVT/loFUJcNzQgwSWdMKrCxXx8G1Yj5N2vES7nq9n8O+KlcFEqkEwS7gFdj6h6L8qwuYLlcj99cQ+JSoiVFSX8JTe58CYMW5FU5hksJCMRZfUtJkjCVoNSxtG0qJwcS9dWXYTSakKhXerkoyy+uvee8AjEkXrtvnn8LUQG/6eWnxlMtbrq1kt8PxT8FqhD6Pguyqn3NlBlSK9XTYMR+63+U0hMVsw1Brxt235ZL15qwsBIuYPly3dy/BPWezSmXiSLyGI2iYGhpDVP5lpKt/gtvudNg3vSqdzBrx+K8fe50REY6ibSkpKZSViR6irCxv2rR5EJncjYhwUWbeeKkSW6XIg6o/UYz7sKYMtAj3psmh8IiRg5t3021UOPmhKuQyCZO6hNCuXTtOnTqF0WikXbsWiMyX1kN5g1F8dpVYZwdQt/ehbl8+ikBXClKrGg3n/JQqxjwoiujdtvJYo4Lt4ruqG4fclr2Nx7o+Rrh7OKa0qkalL2NaFT+ofuODMx/QP2QABw9OxGScxme4Ij2XS22iGza9njq1C8pasHjUUFkdxF51Ft6mtnwZPJk5hb9yqrgTlSFqJquTeCd3KahgcrCCA7298Q3ypFjRHneLgJ+HN2U1lQwdPwWrxcaBNenUVZkYMac9alcF53ZsIee8mGHUplsPynI9uXJ3931zhO7TeuLhp6G4eCPl5aLRnpPzCW3aiBk8eypqufW8+GxfiQnmrsBn+OGVoxh0R1k6PJTESXG4ysWqzT1nzKJg9UI62I5AtZ7qeiPni+rp3caHUy9NJzPzbcyWSNrGzsdgyMVoKkEQRCOprj7N4ZFJJBIUEgm2VhYHET6urLqnF/d+c5IR7+xneo8whsT7E+yhRmeycjqnilXHcsmt1DN/XHtm9olscZyrYbfZqSioo22PAHLyi9BY9YTGxPyhfW/gr+OGgfIvxpABPSgruZ/cnz7mrSXvMHf+M9RVGtn1TTIuHipCmxXq+3dhsLcbizOL2FOpcyy2JVNAl1tb3Of35ecozxO9FFfzKfSnSzE0lCCXuascJhI3HzUu7kr0tWYSmqXxXoFfmBu3LeiFIAjXTMU254vHtpYamLj8IGvu74Ob0o0AlwBK9CVMiJ7gtM+0adNIS0ujV6+mqr0SiYS7QnwpnPccxRs2UIyoGfL1nJ5cKKihW/i1n0fA88+hCAvDbdiwf0uYLkh1jdBM1l7Y2kAwzT4Id6xzbA/uBoNfgOpcGLnYaXebzc7axSeoLtET3t6b8c3Sr6/Abfgw/J56ColcjvesuwiSSpmZ78HB9DwAZlgleGeVEv3DD077dvHrwsz2M8nT5fFKX2fl3w4dOnDp0iWMRiP9+w9Eo3Es9qfp4ochpRJsdrT9Hd3pE2Mm0sW/Cz5qH1Y/JxamW7szi1+14sR2saCW5/q24U7tCCSeUjSKFrKkOtwMlzaIVYq7NRlXnqOj8LgpAolMSnWJHrlCitViJ6xdE4mzrLaJQD617VSSK5LZkLEBd6U7ga7iwsO1ZxCW4npk7ipcugewccNGAA4WHCDYYwY6Yx2uFgkXrDZijteyaNCDlHrFMn9XKmmyc9RYZch8cmibWcyW0P4U+3mSro5k0ow43ug3FHbWIFzeyaXIWhQuNmpqjpG3ey7j5L8RXqOhYIsXo4092HubH5cOisb6bx+e45bnEolO7M2pTRuw26yEtu/EfbVZvC9LIsmez1KXjfRaMZHPX16Mt88AFDneWCyVBAbe3Ch82PzVlyLBUGfGoBMN2YzjpQxqyP7buXMnhw4dAkUCgyKqIXYEM744SUqxyM26PK8vikUp2NPTyXjzC/INy1Ffnkalx0os3UwMbXuTwyMz2OxUW234KVufujoEe7Dt7mgurX8Hr9P7CDklkr9zBX8EoQt929zKR7cNoH3wH+dIVJcYsFns+IZqOXFGJMh26nwNZvYN/CO4YaD8GzBt6hhWlJSg37+OTz/x5977ZlJfbWLLJ0nc/Gw3fIL/vVUwO2o19PZwZUlmEQO83HD5A6Tda03GqmgPkEvBakfT2VFpVqNVMnNRH2xWO6pWYsKeAdd3sfrc0Y49m9J4raicwnyB07lV9I32ZeOkjdSYagjSOqs5RkdHEx3dsriWpcQxxKVVyenditx1cygjIgh88YXr9vu3wCsSlFoxNNd+onO7RAKD57W6u81sp7ZMrIice6myxT4SuRzf++512DYx1Id+/h5oZVLUQ95tdXyZVMbcHq3XAnJ3d+fuu+9utV3uocL//tZl/694UXqMi+LU1hz69/fn18Oity4+yB396RLM2aJCav2pUrS9r3pHvKPg/v0IgsCR6nqC7CaiXERDRtLwm/AMcGHm4r7YbQJaryYj5+s5PdmbWsrkrqGoZCoW9V/Eov6LHK/fXYnPHU0Kt493e5zPkz7n1vhbGRHWl8uFtRx68yyCABkmOxeD4lDUlnG6bD8CZhSAZ10BxVG96FGaRPfE39lWqKZ/r/0oFSoYvRQJEFW+m/LyPRwqvokRUpFE7O5hoAAv2imK8I90xzPAheoSPX0nR2Oz2gmJa8eTP6xvPLeJgRpkGz+gm0XCu17w+d2iQaN1jWXggBNkVmfy5Hcv4qN3Z84djzPYL5Z1XaJRSaUkNhDuh8yMpTQrm75TmxYEVzxkANy7CyQS6o43pbjXHTiA/qhILq5+6wcKhtzO0MKhRABrKjScCTTQrRmhv8gkGkG+LdQXA0AQ4PjnuO2YTy+5CqHLWKpcY6g32wiszeCe3G1I8rdB1nwIetQxjesaKMsTDSrfMC05v6djliqJj70R4vlX40YWz78Rbyxciiz5IFHTH2LC6BH88tZpTHoLU+cl4ur593Uw/gwu1RkYeyqNgd5urGgfeV0jRV9rpjC9mqBYV07//hNKF1d6TpgiCl4hZoAguT4Z8o8gMzOTkpISunfvjlLZ5EFIyq/hwVWncFcr+OWhvk1pv38BllNbqTmUhNu4qaja/DGZ638UdrtoWKj/5jtuqBLHcr2+cdUSspPKKc6sofOwMIdU7uvBbLSSfqKE4FjPvy1CaCnTY87T4dLJF8lffKapR4uoLjXgmygK5oV5u2AprqfsiwvY9RaCXuiFrJXsr9VFFTyRInqEfusWS49rCAb+aRScgpKL0GkaVOfAurtB5Q53/IJdpmLtkhNU5NcR0ckH7fAg3k7JYcSuZGRlqzDaFQwJyEDi7UnFgDIkEoEXCzQ812cRk2ImNR5CsNkp+ywJQ04NxW0v09G0FuvpS+i1EDTlSej/ZGPfHT+nkrazAI2+hMljJHjNEFVbbbW1pPfvj2C2YHHX0PHYSaQSKRaziZKMdA5nnaTX/ihkSDHJLUQvcibL/rToJXKTzgJNZNjK4nKO7z5C++4dSbFqOJhaxLC23hRX2Rly+CS2klIMWfupz0hjxU2TEDwCmVffDjkSHk5Q8fHkBEI0TZlXFrtAv2PJ9PRwZXn7FgyEvUth7xLoeT8Mmy+mxjeHuR72vi5Wpe7/pEPxx2vh0M/pZJ4tY+aivrzyxFzshjpe+fTj6+/4/xluZPH8D+PZl55h0TMVZKz9lMP+vox7pDPr3jzJ7x+dY/LT3f6tSqTttRo+6xDJfRdzGHEilWejAhnh496iABKIlXNjuvuTtGc7xzf8DIAuLZX+46egbtfuuumpZrOZdevWkZOTw+zZswkIaFlvob6+nu+//x673c727dtZsGBBY1unUA8OznP+MLYEQRAoMFkIUimcKhrn7PoK04H3iSULiXII8NcMFEEQyDCYCFEpW+aICAIUnxc9HepmoTS7Hb68CfJPYA0fBRM/Ru7TsoFRZazCReHSupCb5u+FCCM7+RLZ6c8Xfzz0UzqXGnRT7v9gEHLlXzMsBJtA2Ypz2PVWqtamEfrGgD89RnVBOTu/vgRIkO/I5f4PBwOgCHQl+MVe4nMwVgPivRIEAaOxELU6EIlERr3N3jiWvtnf1z13QeDNrGLO6fQsiwtzKiNhq68g55txqM0afDc9j7LXHChu4EmlbuWi5xCW2aoRvGzsmd0eF7Wc2/ILGEgY3fw74ivPoNwygMvpGQg1sSSPDGJYVBDj2oxzPE6dBXNOLTIkhKTF4vrGVjDp8LAYQdvk0RQEgRPHCvAADC4BFC98WDRQkn9DVp1L5I+rMOfk4TZiBBKJ+D7//u4bZJ4+gUKqwhz5ABpBjYuve+N4NruAvOHdrylppmRsroeL6+GUDx0ve6I/n8eDUh02O1Sf/InX5DuwFGhQ+HXFM6GK3bc+xObTQbjUm3GR5RB64RDP77xAwC37HK5VIZVwS6AXn+eXYbULyJt/d7L2i8bJkJdg0LMtPzSlK9y0CLQBsP0lCO8LV4WRWkJZXh2+oaKxI6ksRBN1Q//k34EbWTz/RsjkMp5evBC9WyCHPn6TnJJ8xj3SmdoyA1s/u4DtT3wc/wmM8PVgS2IsoWolD1zKIe5gEl0PXyTh0AU+bCnLB/CPjG78eGnWrCNr8s3Ubt163WPl5eWRmpqK0Whk9erVrfZTKBRoGtQeo6IcDQdBEEiuM1BndVaLvBqvZBSSeOQSIXsdMzxKSkr46kAOPzCJXxjlqI4K1Bgs/Homn1Ld9WsTfZhbSv9jKUTtP4+xpWd36H34dCC8Ee54HEu9uLIG5LlbSe/XH0tBgdPu+/P3M3DNQBK/TyS3Ntep/V8Bu95CxY8pVK5NRbC2/j4qmhOlW7FNrdY6jp+YxK7d0ZSX73Fq19ea+emNE9TXNdTwueprlFqso8ur24l8bhMF1YbG7Sa9heykcqxmGxz7FM/Po3k48GbAToeBIRQVr+fQ4cHk5jYo4P48B5ZGwif9AcjMfIfDRwaye4/Ik5gZ5MVLIUZ+6RzGoGZ1XirMVgYeSyFwz1kOVumczj9Vb+TdnBJ2V+rofuSSU/uDe19jdJv+9O32AM8qn8RiavZOHV3OlgtFVBgsVAp29qWXoZBKuCPUl2+GunPcsxu23SVYC1JZGT6HB97dwvujvmRx/8VOGityDxXuoyNRxXsROLeh4KfKzcE4AZGve6CXBwZpKX4Fa4j86SeEsjT2fnaAL78LpmLDAtzHjEHSLB1bX1MNgMVuImBEW9xHRxL0RCJGi41xHx4k5sUtrDwgkmVl98/l0l1PMvm9z8XJf8NDeOdPR4IRFRCosAICyxSfUrmjEr3bXOqZSoVpPmMvPcPPd8ax98UxvPXa7Qy/615OJb7Kp4/uw3jl/WhAV3dXaq12isyO29n1KoT2hAHXF2ikzyMQNRB2vSIasNeAIAiU5+vwDdNSUVWDq6maoDY3CLL/DtwwUP7N0Lq68MCixVjlan5eshCTzMjoBzpRkFrFvlWp101d/acR76phTZdojvRqx5LYUGYEejPQSyTRfp5X5nQ+AVHRPPLVambddi/e9eIHV2hI272CyyeOkrR7O3Z7kyERFhZGdHQ0UqmUadPEcvZWm50jGRXUGJo+NDKphHtmz2bUnbNxuWmcw8T/aV4ZQ06kEnMgieqrjnk1zusMLW4XCX7ijKqIHQaxjpklT689x5NrztFz8a6WdndAur5pwrG09NxqmuS+sTQ7H5UbTP4Ug7I76RtFQSlbnXOadHJFkwZNalXrKdj/JPRnyzCcK0N/upSaHa2nw8fdFIZ+uD99n+mMvJWwTF1dCjqd6DG4lOzMRclPraQsr479OiuZfi6EvOaY4bU3tZRqvfhurD/TZMD9vvw8mz46z6eP7YPMvY3bZ73eh/63xJKV9QFGYx7pl5eIDdmiGN4V74Wu7qLDcZadWMqnR+7l/t8GO2w/o9OT1vCMF14udDr/CLWKzlo1CrvA23GOBHCd0cLBnCzqa+7FbBzImp7DsZ76BnuDNXcxv4ZpHdxIjPBieDt/RnYIgPxTvOlWwYVJ3Xluwa08OeEF7o+dS7Dgg25fHnZjy+98rdHC5JMZ9EjJ4bec8hb7AEglEj4Z25HIl29i3PoVaDp1pFbvykXDKAx2D9bnPuK0z7gn5pE4eSA3ze2DZoA77oPE6yysNnCxUOT3LNqUTIXZygtlBjZpfOibUgKKJl6ZfaIvofP7cGdnAeOgILb4D0QiA6tOvKdmmw6LXUpibBj+bmokUilVliZDsf6qFO0ErQYXmZSPc0ubNlZmQv4J6PsISP/AtCaRQN/HoOQClF5b66muyoSp3opfmBunz4iGaLtOLato38A/ixshnv8AggJ8mPrCQta/+hyfzn+RZ997l6Ez49n5dTJuPmp6jP33cyKiXFSNBEFBEFVW518uwCoIPBjuqMqo1LjgPWECSnd3ZJ6euHRtKvBVkpXBhrdEsmDKoX3cMl/MHlEqlcycOdNhnEWbkvn6cDYAGUvGYDXq+fyp+4iypTM2JJVUl0iiEr+iaKgol59tbNJnqLfZ8byGNMgbbUP5rrCcif6OIRB/f3/uu+8+TCYTkZGRTvvJWwhV5Sdf4OfF8xHsdh7+4sfG2jLz2wQTqlIy0NvNSdUSgKEvgUeoKI3vfpWoU8I0VLHj8Qlbh7pdPOo4Zy2U29rdRr21nij3KKcU3T8Ds8HKhvfPUppdy8QnuhAa37qkuCraA4lahmC0ORSQvBqLNyez/mwhH53MIX3x6BYrwbq7dyEk5Dbq6lLo2OF9p/aIjr5EdfalpsxA93s6NhJTr+DmbqGcyK5EJZdxz4Cm34TF1GT4bvC5G7uthmOqvrzhJXJHQkNuJ/3yEoICGwrfTf4ELv4CvUXF0raxL5Pvsgo/P9G1X1jvbHyAWEPmjiAf8oxmPmqB76BGwrfHjVjy6nCz6iC4KUynksuQlN2F3GCBrArc+vuhUWtYbRxBDe4U2f2Y/Pu7vD9kDiHx7dEd+ZLiuYuwGmWEvjYb1agX+OSBQRy6VMKo3SXUbMmmZkt2iyGwjNI6MsrqkSgqePn3I0zu6lzXaPnudH45XcCC8e3YnjOfV4qOMLfHXG6Pv4OYLsVcPlvD6PudVaNdvFywBayktMZG6YEvGTZUrLcU5evKsyPjuJBXzSuTOuIulxHnqial3sitQd4Qu5Ca4C7cfmYpOWl3clPpSBRFWdB+Pg+0X0D/208x+PBuFHkHqLNW0W3KOwxRNHFNEkdHIpVKCGzjgU+IYxKBv0rBkxEBLMsq5unIQHyVcigUU6aJdLw/BoOBn376ifz8fO677z58fZuFMyP6ARIoPA0B7WkNV9SgfcPc2HIgBatERkKHGzV4/h24YaD8h9AhPpryh5/j6Iev8fYLC3jh7dfpVWni2MZMtF5q2vV1zkr5d0EikbAwJgQpEhZnFtLN3YVenlqnPm4tFKVTu7oiVyixWswEXMcNWqU3O/xbV1GOqaqGdmHiyihOn41Xs4l/XlQggUo5PT20TvH+q9HWVc1rsaEttgUFtX5v35rWmYlpwfRqltGTfuwwtgYtkKL0NCISugDih3Jem2s8J40n9H+i1WapRoP3zNYVa92UbjzV/anWx/+DKM2ppbQhm2XXt8nctaRfq30VAa4EL+gDXJvw3Ly+TWu9pFI58XGvtTqGSiNv1BVp7Rgr7+rhtH3swwnkXqygTVc/7vz2GKcVN+ES9gWbvlnDwRkHCQ+/m/DwZtlBMcPE/xpQU2DCjal4eYpG4fze89lweQODwwY7np9UylvxzqnxV2DXW7A0ZHfo9uThMTKysU0plzJ3RDcWbBS9Ndv7d4RhZ+l57jDbd+0htiKZnXsN1B9YwK5Ot9LxbAaPlIn3NPvbldT6nqJb1x+I94+m+GQVtioTipCWs/06h3oyuU89O6uXAXC0KIzeQb0b2/VmK29tF/VE7vrqJG7tjgDw5ok3mdl+JiMf6M5I52EBkMk0qNVhGAzZ+Po08b8kEgm3G2TUXTJgyTiN4pW+7EiMQ2ez4a0Qp5XS0C7knDbhZvQmZMdAVNYxfFX9HseU3fHMDcPqMhqdfjVKoMdwR5kAF3cl/W+JxWa1YKyvQ+3qeO23B/uwJLOInRU1zAjyEUUJJVJwcTS+s7KyyMwUQ1AbN25kzpw5TY1KF5GTYqhu5epFlOfpUGsVuHoqKc/Jwubqh/pa6f838I/hhoHyH8Sgft0pK32QrNXLeXvR28xbMBddpZG936eg9VQR1r71le6/A8+3CeKMrp7bz2eyon2Ek7R0S/DwD2T2u59gNhrwDbt2Gt4rEzrQNcyTfjG+yKQSfELDcRvVjbVphdyisiDtNJvz/ZpWdV4KOU9E/mvF7bQqOaM7ORodnW8aS2l2Jh7+AYR1+OdqEwmCQGFaCm4+Prj7Xr92SEv7pxzah9VipuOg4Y0ZVQAGQx6XM5bh6ZlIcMwdxPcJpKKgnlH3X5/c90cysZ4b3Y5RHYNoG6BtJEn+u+DmrabDgBAMdTpizq0lM7qKK6ZucmUyvYN6Iwh2BMGCVKoi+cAeyvNy6DnpFsrzcln9skig7D16Av1m3UegayD3d752kcWWIHFV4D42Ckt+HR6jmzw85rw80rbswcWvDe8OceGtMwJ93tiDtncAEtdgsIxAp7QS3LWc9oUXyalRUR/Qnptd9+JvqGJ+oj+ajGTeiThAkN9wAh7rhrXKiKIVRWKpVMKA9nJ2Hhb/nVmd6WCgaBQypnQLZd3pfF4YE4/N4yGOFB5hfu/5171GqVRBr56/YzZXoNE4GvymjGoAhAaPlkIqwbsZPybWK5a3Br1FwblaDGe0eMkkeFcMpofPQVyqtuFeUcCSiTGse+CnFo9tMRr5Zu4j1JQU02vydPrPaPLAeivkRGqUJDeEmdF4gmAXFYGbGSlRUVG0adOG/Px8Jky4SivJrBfJvBrPa96D8vw6/MK0SCQSbGX5yAPCr9n/Bv45/C0D5Y033uD555/n8ccf57333nNoEwSBMWPGsHXrVn799VcmTZr0dw71fxZTJ4/k05Ji6vb8xIqP/HngwbtEjZTPkrj5mW6NzPH/BBRSCd93asN9F3OYmZTFjEBvXo0Nwb2VTJ8rcPdrZbKtLYIjy0VyWtuReLoomdWv6cNuPHeOqbIYPF48gEzb8se4svIQ1TWnCAudiULhGL7RW/Q8d+A5kiuT+fKmLwlzd179CjYbgs2GVPnHV0DewSFMX/hG0xiCQGVlJR4eHsjlckwmE5cuXSIiIgJv76aPY0m9SDQOcA3g0v7dHFr7PV1GjqPH+JsBSNq9nR2ffQjAoLsWknbCRsKQUDoODGm41koqKioIDAzEzc35Pci/lMTmD98CIDfpHGMfa8pcyMn5lNLSTZSWbsLTsyfD7mrdhX0F1Xoz688U0C/Gl9iApuMJgoBVsKKQijE1i8VCRkYGcaGhaNXXluC32ewIdqFVnsragjKeSMnDX63idN/2SK8yjqyVlei2b8e1/wCUoeJ9sdvtXLp0CZUEfKuzGH/JyhmFB8MHT6dXYC9sNgMnTk6mvj6dYJ/H2LxclNk/vuFnJvhGMOZcBmkBXnxz4Vt65I1GGda6lwRdCQg2pxCd2Wjlp9dPUl2iZ+CMtvg0kwk49vlsPtM9RJfd75Gt9qcgUKxTVHupEmuMO8oGLsk0637u9fqNuTYFycoQ/AzV6F21JFYMw1qr4HBkBWM1dSQf2kt4hwR8JC17UPZX6ngoLxy110zead+Rm2PGOLRLJBLentaZZVM7cOnS0xhXbGfwCVeUTxyH22Jbv/YGyGQaJ+MEwHNiNPVHi3BNbHnRYC6qp/u+UPq09eL8wBrCz5cjJZoQXTS6k6Jey3vn/VrcF6CuupKakmIAjv26xsFAAXCRSTFfKT8Q3BBmzj7goAmk0Wi4805HheNG5BwCBFHQ8Booy9MRmxiA3mDERV+OR+Tga/a/gX8Of9lAOXHiBJ9++ikJCS27aN97773/SEG8/0Xc/8BdvFlaiv7AOn4O8GfSPSNZ/84Zfv/wHFPmJeLmrb7+IP8iuMplfJ8QxY9Flbx8uYDdlbU8FhHARH9P/JSKP1wMDoA9i+HMd6KR8lQKuDd5KuwGAzmzZoPZgOro07j6m2Hix9D19sY+Npues+fuQRDMZGW93xgPv4ITxSfYkydmizy972nWjl/r0G6rrSVrylQseXkELlyI14zpf/g+lNeZeGNLCtF+WtqRx969ewGYP38+W7du5cyZM43/lslkpFamMmPTDKx2K6/1e42KdZuoLStl//df0sOyFSozybc0VZ07vysDQ70f+35IpePAEIqKivjss88a7+8999xDaKjjJOHi4YlUJsduszqF07y9B1BQ+CMAGrW4n9UuMOtCFjsravmoXThTAr2xlOmx1ZpRtfFg4caLrD8r8jEyloxBJpVgs9u4e/vdnCo5xd0d7+aJ7k+wadMmzp49C8CCBQtYd7qAF35NYnxCMG9P69x4DgadmbVLTlBXZWLone2cwpbm/AIip97CruoqnnhyPkJfZyOq6IUXqWu411cKVx47doxt27YBMPq+x6ioNmAUIohXBSGRSDAaC6mvFwXb8oo/xN1vILVlpXQeNgrru6J2RduSKl5uL+fnD5dRlJPJiPseJWHYVYGO8nQx88dqhLFvQ497Gptqyw1Ul4i1Xg7+lE6nwU3P5qimIwEVShCshBnySag9j3fv4eTGaFErbMjKvifQ5M2s2q1IlRLm64OxCLFUSWVU+PhgbciiEXIs7D7xKckHxHf6yR82IJU5G3rbK2pAIsfodhOBPjFi+q/F7pD6rT95kqq0vZS5/k7gLiVQj/7VRezUV5PeQ8rHZz/m8W6Pc0+nexzG1lUa+X35OSoL67njtT54+IkZdrW158nVfUnQsCmofOI4e/Ys69evx9vbm0cffRSJRIJuZw7G5EqMyZX0md+Lkswa7HUW8FMiiYlFuJyO3+OPO13PFXgFBjP8noepLMijz9TbHNoEQSDPaGa8X8MU5t0GQnvA4eUQP/76RFlBgMMfQEBH8G+hDEIDjHUW6ipN+IZpOXs+BRl2YtvfIMj+u/CXfLN1dXXcfvvtfP7553h5eTm1nz17lrfffpsvv/zyb5/g/y94+oWnqA/pSNa6zzh47BRjH05AKpPy+/JzmPSW6w/wL4REIuG2YB/29YxnoJcbL6cX0OnQRdoeOE+b/UkkHrnUYhqmE4KaJq8rAkqHK6oJ3HOW4KOp1AeFoNBaReMEYMNDlDVLJZRIlGg04iraTZqA4aJjxkLXgK50DxCrobYkrW7OycWSJwpylb79tlM7iNk02dNnkBzfjvqjxxq3f3skh59P5bN0awo70ppShu12e4tVcovri7HaxZXymdIzdLlJXEV37NYO69n97Ki4nTTMdI3W4dk7kYQRovpmTHfR+1RfX++QQVVTU+N0DJ/QcO5+/zPmvP8ZieMmO7T5+49kyOBkhg65jFwurrzzjGZ2VohclIcv5WCr1lPy/hnKP0+i/KuLeLk6e5V0Zh2nSsSU6C8uiGm7VqtjNsk3h7MxW+2sO53vsL2qWE9dlZiBcXJLttPYxgtJuFWL9/LFwzsbP0YVX3xBcoeOlH3wIdIWPEeyZpN0WLuObDKFsfpkAfd+e5J6k5WXchXcLlnHzy6LGTzkGDFP3Y7twd50vH0q/s88gz08mF9ui+Bl/3soyhH5CVc8WQ6oyReNE4BMRz0OnxAtvSa2Ib5vENOW9GFTWTVVDZll7p4hWPxzyY2YwfnOY3g22pcV3duwu287NvfoyNMKD7pUemCSjuB8VH+stgQkCjUvPb2UFeOnkxQcxdnQGCb3jkOjvb4H9Z5QP4Z6u/FgmB+9PV3Z9tkFPn1sH5s+FgskWquqyJk9h5pFK1GmSjnQQGo/G+aPoa6Ory98DcD7p52JzLkXK6gsFGtUnd6aTZm+jHu238Nvx2ZSUvIbZ8/NAiApScyQqqxsUiRWNysNIFUrCHiiG34PdSbkqZ7E/76RdinJLZLDm6PziNEMmXUfaq2j9+hYTT21Vjs9PZptH/Yy5B+HA29d955xZLmomzJswTXVZMvyxe+aX5gbKReTEYDuXa7vjbyBfwZ/yYPy8MMPM3bsWIYPH86iRY7yznq9nttuu42PPvqIwMDr8wVMJhMmU1MaWW1t7V85pf95yOQynn5tAcuefIojny7D76U3GPdoZ35Zdootn15g/KOdkcn/s1nhIWoly9tHsDAmhINVOvKNZhRSCdvKa5l6NoPJ/p7MCPKhg1aDq0zqLF7W816IGwOufiAXJ8Md5/dwRSit/O3FtDcLCKW/YMo+xNiwJ7l46CJvxYXRPeM8m5e/jbt/MJPuWIzpVzMVJOM5MRptH9H97q505+tRX7d6/uqOHfB7/DEshUX4P/tMi32MFy5gOCdqpxS88DzVT32BodZMYlfPxj53jelPboobMTExKBQKbrrpJmJiYggODm6cPAeEDmiM8U9tOxWpREr3sZOgvoLi17Us9Alma8ELuCn1UHOWUa7Def+p/bSJEleU0dHRTJw4kaysLNq3b098fMurtlbDaYBU6mhwRGqU3Bfqx7GSIjaceRDZ/jQ0PI2BIdj1Fl4c05mRHQJpF+SOrCGbyVPtyfM9n+dY0TGeSRTv2bhx44iJiSEyMhKJRMK9A9vw6m8XmdglxOF4QdEe9BwfhaHOQu+JziXttUOG4HXHHQhmM3Evvdjohav44kuw2Sj/+GPizp3FY+JENJ2bPLWJiYl4e3vj6emJr68vPSJMbDovioTJZRJ+LhGNnl8N8Xwgc+eZQ3Mx2818l7OGpLuT8Ll7Dh0QV+EHdZ4UpiVz0/2POd/ANoMxdZ1P5eZjuA29l+ZTpEQiIXF0JACzkjLZWi5+t4qHdOGu2Y+Sl5eHf7ULunWiASRRNv0WTtS40NPaBYu1JyHnoTp4P6qSGLLdLATZ25HlK6fcXYYsvAuDZnYjpkdv/CLatOg9EZ+rih86iyUdBEEg56JYpTv7vGjAS1UqZG5u2Cor8VvvT8ageD4fO5UJ7ipeGj+Gsot+rDy/kke7Pep8C7r6kXmmDL3OTO/J0azLXsuxomN4ulsY7dH0jg0cOBCj0UhcM4PDNTEQl64BjcVDZVolsj+hVtwa7ILAsqxiojUqens2CwVHDRRrT+1ZDPVlMPh5J9Ksk5LsdUTayvPqkCulePi7UJKViVntjYf7v7c0yf/P+NNS96tXr2bx4sWcOHECtVrN4MGD6dKlSyMH5f7778dms7Fy5UrxABLJNTkoCxcu5JVXnFe7/xel7v8ISkor+OTZJ5EIdma/8Q6yegUb3j9LbPcAhs1q918ZNrMLAt8WVrAit5SchlRgpUTCV52iGOZz7WeYu+d9nq9QEmUo4NWx9yD1jgTEuPq0c2IIZ4rahVs27CU5by96ay2qPo8wqVj8MPnM6oBEUobcxwd58xTCPwhBELAJtkbxK7vZTPH8lzGmpiKf9ya/fSN6XCq8ZHwpiFoln9zRnVEd/xpZ15yXx6Pzf8LYQc3C+o+JIg+9zYevy1bSZtyzKFyqnUJX/zjyT8JKMatFULhhGH8GTUffRjXgwrQUzm7fRKchIwjrcFUI11hL3i9fo5NGEH/LeKT/AoJs5bffUvbBh/g++AA+16jV0xxV9WY8NAqkUgnfFJSzuqiSF6OD6Oep5bZNt3Gh4gKJAYl8NeqrPzRevcHI8i9X8bvJjaISDd0rclj9+cMt/v5uP5fJrsomA+UKBEHAlFmDzE2Jwl9MTa8yVtHjnRU8VtuLmxEn6yzTY3hvNfD5pFuJrRLTZIct6km87x+bCM3mcgyGPNzduyCRSLh8qpTLp0rpMTYSnxAtgiBQci4blbEKz15d/9Y3JK82jwd2PkCuLpetkzcQ4u5seP4rIQgCizKL+Ci3lLWdoxno7XZ1Bzj+OWxp4GOFdIcOkwEJlCVDyiaRHDtsvijWdp17sePLi9SWG5gyN5EF9z6A1M2LBe+8/q+5uP8D+I9K3efl5fH444+zY8cO1GpnXsTGjRvZvXt3Yzz+j+D555/nqaeaUilra2sJuxZp7f84Avx9mPbiq6x7ZS4rX36Jp999j+F3tWf7FxfReqvoPbHl4nf/SUglEmaF+HJnsA+X9SbS6o2szC9jXloe27rH4XONyqPhAx9mVdoW8L8JGowTEDUoFseGYLILTPm9EAvtiAlrR37dg3yQZaG79BQdXY9hTruVwrliQbyoDRtQx/1xfQKTzcTMzTNJrkxmXo953NH+DqRKJcFLRUKsoc6M1ruUuioD0vhCuib7UotAbm41NDNQBEEgP7UKFzelk2aD073SatFLrOQEh7CpeADd4rdgUMOw3QbS9D7Etm9KO047UczJTdkkDA1rJM7+IwjuJq4eKzKQjHsXF1dHw273V59SkplO8oE9jTVVriBv26ds3NcBm/EoOzb/TnzfjvS+eToe/v9MdpVgE/CYNB3v1oiNraB5eGqsoEb6fRGltgJqX+rJt2O+pdJQSYCro66LtcKAzEOFpJlnUhAEPj+QierHz5iwZyMTgNtGzeeYbxTGrGo0bZxD2svbh7OzopZBXo6TpUQiQR3t6bDNXemOt8aN5bVGTmHlMW0R38pCKZiTTbDBBA3Rwzau6sbzKc6sxcNPg4u7s/fBZjNy7PhYzOZy3Nw60LPHRmK6+zeGCgEu7Ctg/+osAGbGGHH31TiNc3HLGpIOHaXvjFmEd+zs1H4FYe5hbLp5U+O/a2pqyM/Pp23btigUCkymMurqLuHl1Rep9NoE6j8LuyDwXFo+3xZW8GpMsLNxAqLB0es+CO4CX4wAqwn2viEaLt5R0HWmyCXy+mOF/sry6giJ9cRmtaHRleDS4dqE2hv4Z/GnDJRTp05RWlpKt25ND8lms7F//36WL1/Ogw8+SEZGBp6eng77TZkyhQEDBjQSC5tDpVKhUv17C+X9t6Nd20gGPfIch95/lXdenM+L7yylz83RHPklozHF8r8RUomEtq5q2rqq6eLuwsiTaUw7d5mvOkYRrmnlGcvk0G6802aJRMLdoSLDv1jdVOOjt28eTxirSK7pRJiqO7JTJxvbrOVlENcWg0FUEXXTdiAysuVVL0BRXRHJlSLxcumJpdzRYBzY6urR7diBS49Ehs9N4MvddzC6PJJZTBWPs7+MNR4qOnYOpINWQ9qxYnZ+LY4z/rHOhLcXNVQEmx396VJk3urGiUru5cXz82/jgc0H0bfVY9CIbnubfzrDxn2Oi0vTh/PYxiyEjBSOZ2bTceCMFq+h2liN2W7G38U51GM2WrmwrwCfUC0RHZrV+pFKYfhCBLtA9YbLGFMz8bm9Hcow8YMf0akzJZnpKDXOE1mKrJbRbk/xa7VIyr2wp4gLe3Y4GTJXYDdaEcx2ZC1MriCSjwVB1DwRBIGyT89hztWhjPLA//4E7GYzgtnSalZXYV0hnipPXJopl+ZeLKePUoJaKkf37mk83hjgZJzo9uVR08CLCVnUr9FIuVBQy5LNKdxVmUnPhr4K351ILb3R2wWuviM1Fit6m51bAp0lAXZ+sYJz2zeRMHwUI+4VVVplUhn7Hn6QfanFdA/X4qPWoFuzmjwB8oVNBISVMb3fdJQNpQTO7sjj8C+XAZj1Rj+noqKCYMFqFb03Op2jQu4V6Gub9IZMBkf+UHp6Oj+t+QH1xZMIdoGfXnux1WdZVFeEQqbAV+PbcGyBr776iurqagAWLHiZk6emYDQWABKGDb3c4jjXwxUFaqnUMaSVoTfxbWEFtwV500Gr4ZxOT5yLGnVLXrywnrDQmbf1Z2A126gurqfz0FAupGSgECy0+RMLoBv4+/hTBsqwYcMayVBXMHv2bOLj45k3bx6+vr7cf7+jnkCnTp149913GT/eeRK6gdYxoE83ysse4vKqD3nrlaXMXfgcdRVG9v2YhtZLTUTHv1a99t+FULWSn7tEc2dSFgOPpzAjyIebfNyJ1KiI0ij/kJv5q4JyPsgqopP5HGOUWXSUbGCteSpta81c9PidYW4nCZHkopn7FPK2iWj7iQJkuXlfUFa2nbKy7Xh790Xr1pkn9j7B3ry9vNr3VSbHioTSCPcIHu36KMkVyTzf6/nG45a89ho1GzYA8MmQaezoOJN10UHcYpdzV7aZ30LkLDbXwIkatie2RW5ripLarU1/1x0pouZ3kYPgO7sD6jhxEsvat4m6kAiO13QjXCgk2sWHog7LKTr6IYMGnkcuFyfjTm5ZqE6/KY51IATtAEeVzKK6IiZvnEy9pZ6nuj/F7I6zHdpPb8vh1BZRrn7KvO4ERjnq2FjL9NQfE9M4Sz8626hSOmDGTHqNGILSz3mV2Se2N/JjH+GlDKbK7IJEYSM4uuVMPludmZJ3TmHXW3EbFo7HCMfxLpfWMe7DAxgtdt6cksAt3UIwF4phNHNWDbaaGjInTcZaVIT/c/PwmTXLYf/NmZuZd0D0nm2dspUQrWi4x/UKonRfPthbj15bivWNfwtWe6OBEuatIdhDzaq4wWTHXiLXX0KEUUao+gg6v1H40GSIVJitDDieTKXFxsPh/syPdkxFvrhvJwDnd25tNFAANEo5wSzm/Knt+PgM4YXEDzjwWTZSm4JZr/dj/qkX2HbkSe5qfxd96yY1nbPZuQaVXO5G167fo9NdIjhoaovX2m1UBBo3Bb6hWvwajNDKwnoKL1eTlHcG34CLKK2VVCR74eHqhiAI1OvN6H9MxXy5Bt97OpHins3MLWKa73tD3mNYeEOI0IEhIGCzGRr/btz6JzL9astL+fGlZ6irqmTqS4uI6NSlsS1YrUAugR+KKvmhSCTjqqQSxvt58lRkIG1c/tmFbkVBvWg8h7ux87C4EOrW9UaRwH8n/pSB4ubmRseOjg/I1dUVHx+fxu0tEWPDw8OdCr/dwPUxecJNfF5SSu3O1axY/hUPPXo3uioTWz+/wOSnuuIf8d/N0Wmn1bCnRxwr8kr5saiSrwtE0t4kf0/ejg/DtRXS3xUszSyi2mqjKC6BNsWZFLv1gJQCEmrXU5rYlYjc85jt0Uh1ZjRdmhRHfXwGk5//HQAuLjGU6kvZm7cXgJcPv9xooEgkEu5LuK9xP7vNRnFtEQaV+CGtl6vZ4NETU5g/gkrBh3Ew5dv5lD01ERCNIblEgmAXiO7mR6dBoYTENYUApK5NLm6pS9PfhaeOcUv9FuxRaibP+5Gs7A+oLThx5Swa+0VGq7niO7I1rFKvoCI/l18Wv8CkGm9+HmJgd+5uJwOluStf46pAsAsOVaflvhrU8d4YUyrxmdks1fL7KSgz90BIItzrWJNIGzsK86CHGBq+ksxwFwSFhGFDt9ASbLVm7Hpxxa4/W+pkoORX6TFaxOvdl1bGtB5h+NzRHlN6FdoBoZjzL2MtEu9AxaefORkoGTVNXJ3CusJGA8XVU4Xm8c58kVnKoBg/WtIT9hgVicxdiSrWE2mzKuKeLkr2PjsEi20QRvsMNv58GP0h0WC0ZCugWfSj3GKl0iIaDetLqpwMlCF33UfSrq0kThnCocODMJtL6df3AEqlLxUVB8TrqtiDa+3zYJRjRyArqZTt2dsB+ObSN9w35VFcvdQERrrj6e9CS3B1icXDvWtjEc/msJhsnN6eg4evhuDYhmrOdoH1757GoLMAvrj103B+YHdsA+UEbqhm2vKHSFWl0CPzVu40Z6D4Ko2ie5qu7WL5RYaFD0MikTBmYD+K8vPpM2osEomM9sZnqT13kNB7XwIgx2Bi7Kl0yi1W1nWJpp/XtbOSSjIuU1clGh8nNq5zMFBcZTJ+6SJ67vyUCqosVg5V1/F1QTnDTqTwVlwYU1rwZP1VlOXpkEgleAe7kn/5MiaFluDAP89zu4G/jhtKsv/luPfeO1hWWorp8HrWBAYw9Z4xokbKR+eZOrd7i/Hk/yZo5TKejQrimchAcoxmDlfX8WJaPmeO63k8IoCbfD3EWhotYHaIL+/mlDBFBWPGjKZGVsi9ZUoGGc7w3JurSFYE4zZ6ARR5U7voWKMHwN2tL27Sd/hw73y+OTCGKPswXpU8Qw9jG6rvdkNfW0PexSSiunRrrKtjMZtYtPgufmlzEaLhm/deoHPmWsZkHmVj/jCs7TzpmZFO9aQ+jA03MCAylGitGx5VFlavEgv5FaZXM2dZk5fDtas/Cl8NUjcFcs8mztbwux/i/K5tJI6bjErlS0z0PNy07fHw6IZc3vQB95g8CYlSgdzXF9c+fRzuTfqxwxgrq1Eg5W75OO4ZtdDp/rXvF0xgGw9UCNSuTEJXa8Z3TkfUbcWJqiwvm2OVG4md0I/QDg0fXkEQi64BFJx0GhOpDNmQ+RRpjyHoMwkLm+PcpwHKYC1eU2Ox1ZhxG+hsJgyM9ePViR2w2gTu6hsJgCbeG01DrSCZR3v8583DWlyM76POGSazO8xGJpHR1qstPQIdJfGXVlXxo1HHhxd0XOjXEV+lnMKin8nL/ZKIyAcJDBjvoP7qcN5yKUq5FFd8GNSpN1sOiV5j/2BPh35xrmqWtwunwGjhvjBnwbGEYSNJGDaS7OxPMGaKKdhFRb8QEXEfHdq/RWnZViIjHsJmCKIwvRqlRk5cYjDPez3P1qytDPR+iIRFohfm+IvDnMYHKCxcS3KK6P0bPOgiMpkjNzBpXz4nN2UDIJFKiOsVCBJQquWigSKBkNHvMzdJ7BMenUyKu1hk8Xi7D2l79D0Gud/MTRGVVPWswkXhwsRoUQitNDuT3998FQBTUT7Dpt1B2ROvgs1GxsodtEtJ5pnUPMob0q/fziq+roES1a0HXUeNx2IyMnTOA07tPZuV3IhCRTcPV+aE+vJcWj6PJOfiKpMxyu/6itd/BOV5OrwCXZArZNQX5SDxCr7+Tjfwj+JvGygt8Uqa499dnff/Ip6a9ziL55WT++tK9vn7Mvah7qx78yS/Lz/Hzc92R+36z5LR/hWQSCREalREalT09tCy4HIBT6fmIaTmoZVJuTnAizevqgg7r00Q89oEkZ39CYVFa7hQ8RxgpX+BqO+ABU7L1KzsIaHCo4R5uzsxovcmfvhiNYcrtnE2SgBqUJUUItFL6EEbQi648utXCynOEIW8bu+5kMCnE6mvrCTP1FQ0Li/GnW4jNzJh+3JUFYf4URjDA65x9Jw5Gamq6WdjFKS4uCvR15rp0AKR9QqvozmiuiYS1TURAMFuRyZzISRkBtZqI/UnitF08EHqokAileLRSmi03YDBZJ45CQiMvOkZdn6Wgm+YlsQxkWQUl7B5zfdUVtcREODBXQPvwNbAQ6jdk9tooBxc/S2ZZ0+RduwQcb37i1L5Egnc8g2kbWksrnc1ZDIVvXpuxmbTo1BcezJwTQzEbjJBC84yqVTCnX0ixfsgCHy4O50zudUsmtSRYE8NEokEn9mzWh1bq9TyUJeWzzHGpWmi1jSkuV6+vBSLpZKLF58gMEC8rzlJZ1m/9FXUbm7c/f7nyK9SGLZbMohsf46eE29uFClrjql/YMUeGDiB8oo9INgICRGFB/39R+Hv3yDUp4Wbn+3e2P/W+Fu5Nf5WXt+SDIhex5QiHf5uzokJ1dUnGv+2WmucDJTmpG3fUPFviUTClHndKc8XCaBZJjMuMil6mx2z2ojd5IdUVYaxaBIBIQdh+s/IpDJua+coliaRSMT3RRCQyKRINRrkPj5YS0vRDhokHl/R9Fv5utP1vehyhYKhs/9c2QFXmYz348Ops9p5OjWPXp6ueCn+/tpblLh3w263o6wuQp3gXKjxBv61uOFB+R+ATC7jmUUvs/SJpzn++dv4+i1h/KNdWPfmKbZ8ksSEx7ogU/xnNVL+DNq4qPguoQ0FRjMna+tZXVTJd4UVzAn1Jd7VcRKw281kZIpF0NpInyFStoyCmLGc9gzgZJwvv8a5YPTSAlrWCTPoeHohtRUeaGWxSO2l2KXga/BGW2mgyFdG95GRCMebjGZrmRgz9wwM4t6Bj+NV/Bu9eoxkYoy4Shww7CHSCsp513QCFY9zMX0snTp+0Li/2lXBHa/1wWKytZhlcS0YU9PIvvVWBL2eNps3U7W+Emupnqp16S1WrW0OV29XosadQ6/PJOlEGzLPtiPzbBmebdzZ/9JDdKio5FSPHpSUwJmqacT2WUFxVSGhU0RPhslkIgMV9fHdURXnOg4eO1z8D9iRs4PPzn/GrfG3cnPszY1dpFIFUqkHxvo6SrMyCInvgEzu/DmpO3SIvLtFddKYfXtRBLRcITm7vJ63Gwra9X1jN9lvjP1jN7EVPBTmx0hfd0JUykY9nuDgaeTkfEJQUFO13yu1jOoqK6gpLcYntKnOiiAIbPpgGVaziZRDu1olj14PanUwid3XOGzbW1nLyvxy7g31Y5C3yPsoya7F088FtVZccNzdP4oavYU2XjXEeZ5EEEY6hXGioh5HIlXg7dUPlcr53kZ08GHW0n4oVDKUzUJZGq2SsAZPVZRayYle8SCR4ta7LVG7TaRhxTs6l2yJFF1Eb1rye/hFRDF54SIW7pvP18pPWZgXxKTff8NaVoYqWsw2XBYXxmBvN/p6anG/ymg4UVPPNwXlvBUX1jLR9U9AKpHwRttQehy5xI9FlTwU3ro+0B+B3S5QkV9HTHd/cvKLUdsMhMdevyzADfyz+N+Z1f4/h4tGzcOLFmFWurJx6SvUWnSMeSiBkuxadn5zCeEahMD/VoSolUz09+KbTlFEapQ8cikXvc3u0EcqVRIcLGawbM8ayuCKevxkYVQHTOanIaNxc9fjYxCrH08pzybENprb2MAttnQO9HibB/Z3xPfSRSQlF+nxcl+kLgomz1vAiGkPManvXNI711PUwHPoM2YKb8/5mmmdbgUguc5Aar2RxyICiK7+DIDS0qYUS/SVsH0+irRfWzZOjDWw9Xk4ukIMnTRAEASsVVXojx1D0ItkzZrfNiK5ysjU6/Vs3bqV06dPOw1dX5+BXi8ScG3unzRuV9ur6HsxmeDiEsb/9jtt2x7AaM7lVe1ybpc8zqBfBgNQXV1NvVEUSDQHRTgUGmyOD05/QEplCgsOL2ixfe2rL/DTay/y3u2TWmzXn2ha4f/+eJPLvspiZXZSFpNOp1NjseLnocLu0VDrJ170yuhtdpZmFvFdoaNi8BUYDAZ27NjRKLvfHBKJhBgXtYNYoC7nFvYXPsxpa1O4rMvIcQS0iaHDoGF4BTd5wOx2O6WlpYTEdaCjZ396xk9w8AZn6I28m1XEmq07+O3JJ8l78UUEs2N1boBCo5l9lTrsV3mSX0ovYGdFLdMbtH7ObM9l3dJTfPHMAQx14jj+bmoWjPEimjlcuPAIp07f2rh/ZWE95/fkIbEH0C5+CQEBrRt0ajclHx/MZMnmZIwWR6JtZWUlb731Fh++voS6ogKUGhemjX2ACT0HsiJjC+su/8K4X8cBoK+pRl/rmBlj9FNwRi2mMC86tgiZu3ujcQLgJpcxI8inxSy+p1Py+LmkimdS87D9CU+7IAhYyg0INsd9AlQKRvi6s63872XvAFQX67Fa7PiGuXH27AUAOiW0Lol/A/8a3PCg/A/B38+LW196lZ8WPMsXC17kqXffY8Sc9mz97AJu3mr63hxz/UH+C6GUSvmiYxRjTqXxUW4Jz0Y51mxpF7+Y+LhFfLR2BSGmy6AS+Qaff/I+xzqLq5rb7UdQyxZSNborqzwiGeLjgXtYW4bfJeHYuh/oNr5p1ezq6UXClDH8+uuvnDt3jn2pR3nxxRcdJOvT640MP5mKTYB7Qn15NPJB0tMXExTU5EXg0PtiPQ8A9xCIcOSJWA5/iuKoWPsFuRoSRRJr8YKFVK9di8zLC/cJ45FqNNTcciehWg0U1KOO8QTg6NGjHD16VDxnV1cHlU539wSsHiM5X7iTn6sFlr0oZ2DYQOxmMxdi41Ckp6Ltoua2tJPYunzNusIdjfuWG8p59Nij2LxsTAqexJSxU1p8LrVbtnBfThQvemYxKHyIQ5vdbEMil2Kscy5xINgFdn+bzOVTpYy6dRwVWzdTataTJTSVLdhcVsOWholkYUYh9/jqGJi4C5NrfxZ3FsNfqworeDdHLLhoE2BWiCNB8ejRoxw6dAgAFxcX2rZ1TgGtrq5m9erV6HR1GC4f5ovJd2Atj2DV4Z1s6TucgPAI7pj/Mrh4U2mxMvtsOidq6llSnk72xYuAlHu8+oEJMic+g8/sAcjGDmPa7/cSlXaByKrheKs11GZmMfillwh5883GYxttdkacTKPCYiVYpeB03w6NbSN9PbicW0pHregx1Nc0GTdWc5ORLpHIkEjkCIIZhcKzcfuG986grzVz8MdkpnXLQuruhnX4cOx2O4GBgdjsNo4UHSHKI4qsIhXv7RRDmscyK9jwSP/GcQoKCtA3GMlHjx4lIkIkMke4R+DvFk+BycxjXe+iNDuTVS88hd1mZfyTzxHY3p3ikt8IC5rKA50fIKcmh3mJ80g/UYJnoEtjttC1MDPYh/mXC/i5pIoik4UlbUOJc71+3bHqDRnUHxUXFVd7GhO0LqzIK73uGNdDWZ74XvuGasnZmI5JqqJt9I0qxv9u3DBQ/scQFxPB0MdeZP+7C3n3hZd54Z2l9J8ay8Gf0nHzVjsULftfQnuthlkhvnyaV8bMYF8CVY68Gnu9hV6qAALdtzNecYZ660Ok+IwDRILq49YpXDJXMig1j206Bcuq9BSECsRlLyfOay8c/AWGO66stM3qe1ydAmm027myQCs1WwnwH02A/2jHkw5qll57lfBT+vHDnP5uI9OvbI7o29imPymST21VVYS8+SZvbEnhkw8O4wocn56I3WBDppAREtK0og+4KjQikUgxeNzEtxfEbBCzXZzgpEolnTb+ii3/HPIvRB6AbO0sXn8+l4MFB+kd1JvjxcdJrkoGTzjsdZiZLo5VYgEMZ89S8ORTxACbBg8mbFZTrRpjWhXlX4qrypsfW0Be9gXi+w5sbK8pM5ByVExf/v3bXCa88zaFP/3ATQObjJwBXloiNUqyDWYeDvdnwZ7nOV9+HthM274iP6Sdtmmy6ubunMESFNRkyPr5NZFUBZuN8o8+wq43kNmnN8XF4rlEFhdhVUYCcMbkK4p4rRwGxUnQ+VYO9nuDYzVi3Zmz2WfxxPEdtJalUPT8Zi5GTEFancaES4+jc48m4egKDJpqvJ+b59DfhoDRLhobhSbHelrzo4OZG+FDfs6HZGZupcf4+3HzURMQ5Y6btxqb3cbRoqO8c+odanRSlvZ9jYTIJg+KWqtAX2smoOQEZe99T7WHB9vT0hCAwYMHk+6dzgdnROP5i/6r8dWqKK8zMb2rP8eOHaN9+/a4ubkRHx9Pjx49EASBUaOailcq8s6yPDODrd59+aGuIwmWYuw2keyak3SWCjag12eSl/cl98ado+zX82RuvMBOewEZnhdZcOcThEW1XqkY4N4wP3KNJj7PL+dQdR2DjqfQSashXqtGAmTpzTwQ7sdYP0+H/Swl9a2OqZJKsPwDvMfy/DrcfNSoXRXU5Gdj9whEer0ChDfwj+OGgfI/iL69OlN2xyOkffcey155g+dfexFdhZEDa9LQeqmI6nztD8N/Kx6PCOCXkioeT87lu4QolM0+CFJXBXcN7EjJmSpUKJAZX6Cbph25hqGcUNRxSSIy7MPUStA1+4AZqls93rBhw4iPj8fPzw/5VfyJTm4u/NQ5mlqbjTG+rRBBO06BiP6g9gCF48qvKD2VfL0nH6b2YdILiwnza/J+VD95Gzk/fkXw7bMAyCgTtT+eQ0PVGpGHEfrGAOLi4pg7dy5KpdLp/AAmRE9Aq9Diq/Gli3+Xxu0SiQR5SAJ0uxMu74bp3+KudKefxyC2vJ1EZb6KUSMnkiG9xFTTvZzckk23m8KRyqSU5enYsiIJiWClm1qL1FiHupMoIWCrNaM/X4a1vElDxNXqRteR4xzOy8NPQ9ueAaSfLGXsQwmEtfchbIGjPHi4RsXR3u0RBDsSiZTugd05X34eaTOORX8vNy7064hGKsFV7siyNaamEXjhAk898ggaDw8H71f9kaOUf7wCAL/jxwkaeRN6vZ7RMx6nZN+vZMeFMD/7LdhXBjQYpud+ZNC45QzxdiO5zkhFm2KCUgPp5VtJtXEv3rV+yNysBH+wkqzKO+lW3QuduxjKuNB+Nh+460ju7KjA6iqT8Vu3WM7r9PRxk/Jc0mESfeOYGiRqGFVX7CA7RzxPgzGXzsPEApaXT5Wy9PibXGQrI08JXAxV8oH3Nr6Juo30Eh01Bgs3P9ON9PfP4CZ4ok8VsCgUjcoj1dXV1KmbapptWvQUhz7fjCDAVys/Y0tJCVu2bGHhwoViLamuXUEmc3jHJAfeoF1dBu3qMvgw8jaiB/di8J33IJHK6DpyLBcvpaPXZyKRKDCmVKI3WDleb8WdQLoZfJhxaAKHoo5wPbwSE0Kp2cq28hrmRgWRUm8gU99QYLJWT0CJ3MlA8bo5lvoTJbgkOKf85hjNBKv+ftJAeZ6u0QskqSxE04rWzw38a3HDQPkfxcRxw/iiuJTqHatY/sFKHn3sXuqqjGxfeZGJT3V1EuX6X4CXQs5H7SK4/Xwmy7KKebGZroREIiFwpBc/ZdSRn2TG43IsFT4BpHc8zciu2+mqeZDSXw+jeW0F6xa/T7eoKGQSCUz/DlK3QvsJTseTSqXXLKswoAUp7fnrL/Dd0RzuHRDFi2Pbg1vLpM/E8TdjNZvxj4omrFN3h7Y3jOtJGVQC+UtJ4g4WTuhAXIAbfXKNkOFYLNNuNFCUnUFofAcnnohUImV4xPCWT14qhQlNXo9V339PwYkitCbxXNoe7c1Dtz3O5hVJQDXleTpG3deJtOMl6CrFCr7Kd78nPFKJqkHDqGpdGsZUUYvdtVcgigBXNB2cBQMlUgkj5nRg2Kx2HD16lPxDAn369HFageblfUNa+quolAE81f8wd3e8G3elo7bP1Snop3IqeWvNDhL3bWVMhhj+apeS7NBHFRuDzNcXW3k5/vfew/2jmzxf8wDhg0RsphrRNrn5c8g7BgOexkMh58eGonsFda+yI2wbbfJvwTc6GJfO/sADCIJASNJwbum6hwrdNMoqNazykvDp7KEtPob2Wg1tXRQM/GksOmMh6xWRaEd8zyg/D7Tadshkrths9QQGTGrcZ++qFEpDi7jtmJ1h5wTAiFv4vew6mcd9v5zHZofhYWpeFj4hPGg7zIC2Tx0noKgIq9VK+/btMZoMFG49iCq/nsTOQ1E1GHiaq9SBDRcukj1VFHgLXrYMBoaQm/MVbsqheFtD0LnvYLXXKiSSPmKxywZ06PAu0dHPoFaHYq81o82qRnMmB4PNlRj1YeYYOlP85jJSBoylUldBSJiSHGkOwyOG461uVuVYIuHNtqEcqNJRbLLwQbsmT+RbWcV8mldKvdXmYKAq/FzwHOOcEWQTBLaX1zD0OvW/rgdBECjL09F5aBil5dW4mmvwi/7fDJ//r+OGgfI/jLvvuZW3y0qwHNnIKj8/ZsyewIb3zrL54/NMmdsdD7+WhZ3+mzHA2417Qv34sqCcB8L8Her4WDSefO5r5+F6Lbkd78UuUxFWDRVbTxF9+zL0lkkgd8V2/ACamGj0J09izsvHY/wcJC14IP4sBEFgzQmxeODnB7JEA6UVuLh7tJouOTB0ICmVKQS7igaYn0s9jwxyRymJwZhWiarBuLSYjHw77zEMtTV4h4Qx+50VTmNZKyqw19WhjGi9tojdbic7JZkA1XH0NUlYDZXYLSEoMlQoVEqqzFa8O4iTRnzvQHIuVKBUy4jqF4Nc2TQxNBee85p8/YyG9PR0tm8XRccKCwu55ZYmHpCluJ6ijN8AMJlFnomHytGoFgSB6hI97j6axiy1d7ancaRSy5FOUxmTcZR6Dej0Beh1cry8vFAqlSgCAojdvQtBEJC2UEZji+09skrMtGnvwuiE3pBwi1OfEG0Is3rOoVHvvgESiYTOCZ+K/xgBqfVGjmUUckEtMEAQWlRLtdgtGMwi0VdhySbWVTwnV9dodMELuFyTQQ+3Ju9LqL+V+Mp2FHudBQQKvX04tWs50u16Iv2Gk+EajezyLtSqQ9DguJO5uxPXrDibRu3CkhdWU1pYyPY9e/nss8+48847ufXWWykoKCA8XORTWIqaUusNSefJC/qa2tozlAaBPOlrKn0nsHnDaTZveIWFCxciNFyjRCJFownjh8IKFmYUMGuIL0/1ryNv+weoo8IJfekQX96s47MN4m8k1PtTagKyeO3oayTd5ahG7qGQMy3Qm5+Lq3glJrjxHk4P8uaDnBKW55Yyr40jL60lbCqrocBk4Y7gv6eyXVdlwlRvxTfMjdNnxFBmh043CLL/CdwIqv2P48l5j6EP70Lhxq/Ytf8oYx7qhFIj57cPzzVmA/yv4cFwP+ptdvZWOnoT5FIF/UJ782mXbhglBQCYpRUEu7Zl/9HRKLVjUbrdQkD0SCxFReTcNYui558nc8LEFvV4ynJ1bPv8Alnnylo8j7JcHb99eI4L+/IbP8wvjWtHfKAbX85KpNxs5d4L2TyZkoulhSwqS0EBeQ88SPHiJQj2JuLjo10f5dQdp9g2dRsGQy6HDg/m0OH+5Jd8g0snv8aS9Hp9PuE3naPjnWnoqnKcxy8tJWPkKDJGjqJk6ZtO7cl1Brodvkj8oQsMGj0a77hSrAZRpZOqXDy85PR+MoGV3iam/XaWX8/k4xOi5bYFvZg6L9HBOAHRte53XwLBrzTwaS6sg4Ue8MkAsNu5Gt7e3o2hl/j4+MbtgtVO6Sfn8Dw6BpfyDrSNXdK0U2kyrH8Y0rZz/Lcsflh4jE8e3Yu94f6O7iROVK7Weo61CeKX7iH8unMbK1asYMmSJVitIk9ColS2aJwIdoHcDLFP5iW9U/sV1B8/TvFrizBlZrbaB+DDnBJ2VNTyakYhBVfxTEAMi0myjXw2bAVzEh5h+y37iW7QaCmqK+KFwwv58uJ39PuxX+M+7VK+I/HiKUrjojh5+zKWzr4PfWw8dfHd6Ft/lq6KVFa4f4S/up4aqxru3MCFghq+OZyNzth0DgqlipzCInJycigsLOTIkSOoVCratGnTGM5xGzaMwIULCFq8iIDnn8fHWyTQFh8LZE3WUo4k7W8c78WDL5LwbQKvHXmtcdtHuaXUWu18kFvKpeDODOj0HD1cbuXHPkOo81fy6NmfuDd7M4JrZ241BJKUlYv9VR+xonAz9PbQUm6xUmJuqhUUplbyaIQ/7+WU8Htp9TWfA4jEdglQ0sJz+DMobyDI+oVpyUhJxSqR0an9jRTj/wRuGCj/45BKpTzz2nzqvcM59cU7pGRmMv7RzpgNVjZ/fB5rC/U7/tvhp1QQqVE2EhavQCKR8PGIj/n84QWEjf6aCr+j1Phf5LhPIG6VvRr7abRKJEolkoYJqszgxscP7iHnQoXDeId/uczlU6UNYQ5nnNiURe7FCvb9mIbNKk7AM3oG89U9sQyND2BtcSW/lVXzY1Eln7SQOVD144/U7d1L1XffUbdnT+N2s7mcpHN3ceBgH2p1ydjtohZLReV+h/1r6g6g8TEg19gY9bxjtsKuilruTsnjbKDohak/eNDp+BtLqyk0Wai12slr047YrmPxiqlBpYUpTz6HS/e+5F0qw9LABj6SUeE0RnNI5FJUbTyQqhoMl7M/iP8vPt9ifz8/P269NYhp0yA+vpmHRyJBopShrmpD8iZ/1j7zHft/+Fps2z4fzn4PP9xCVbEzGfKO3hGkPduJ3z3eJCa+mOQBcjSWplCczdby+15dXc2xQ/vQleYwbFY7YhL9mfFyzxb7AhQ8/TRVq1aROebaeiw3NeMn+V6l8yFY7ZR8eJrylRcI+Urgya73E+TSVArBQ+XRKM9/W3yTCJrXjOl0ysrmrqNGjgXLsKmbCiXmB3bhQ0HgiqNG5yHFFD6AGZ8dZcHGi3RauN3hHOLi4vD29kYmk5GYmOh4fjYBY0oV2uET8ZwyBYlEgjbkYQYMPE9Vmki2rytMwWSS0tWWRM7FnwFYm7a2cYz7w/xQSyXcH+pHpq6UHsIR1BfL+M5zJD/kTGVsWRa3Rg5ldd5wBhcHkLvPm5KjrmRkOabOX/GU1lgdn9/TkYFoZFIeS8m9biryYxEBjPR159HkXIr/hpFSlleH2lWBq6eKytws9Fp/lMr/fjHM/4u4EeL5PwCNWsWjixex/Kkn+G3Zq9y5+G3GPtSZ9e+cZsdXlxh5b0ekUmfX838zxvp58m1BOc+1CcK72YdfIpHQxVNLhdaETF2D3aIBCdS7Z7C6TzssKgn3tvNCLpXSZuMGCo5fZvc28aN3aN1lhyKLYe29yU+pavUcojr7kXWuHKlcglQmRV+n4+2XpiLUllEwpQuPDlyORirFYLczwd/TaX/toEFUrPwCAE2zCuDl5Xuorj7e8PcO2sW/jtVWT2iIYzaNv98oSkp+x2yuIDLSMVz08Klk7v5mCdne6YT1SaDPp985HX96kDf7qnQoJRIm+3uhCVpCQrNq8cXvnKJTaT1PosZ7YjS393IMExltdi7VG+ikdUHR0vvT7wmoLYSY4dBCaKO+PpOs7MUAFBWvYthQMdVVIpMQ8EhXqi8XULVUzLA5seFnBt42C6IGwGUxJbr/1Bg8A1wI7+BNSso8Ssu2ktBpBd4XDhFlSyfKHfp3nEtd3Gj8fPxo06ZNq5XR167+gcLiUrbsgIXD3Im956kW+12BplMCdbt3owhtlhVnt0NNLm9f/pmvL33D7NDZ1B6o5Wmtlqefegqd7hw6cxm+viPEMIUgIDSkDNt1zt5MF4ULP/f9nLJ338NDEgoNNrb7qFF8E5DOyvMrcZW9Tal2Kh3dbPSvjONuezgyewnZpoWcqviZEcu+pMRsxdVdSV2ZlQB3x+v3VGiZbu+Ppb4etUFGc8U13f48areJnrnAZxNZUVfL4kwxfXfz9JkcX/8zxbJANN4bCbPm8n2RienSUFQ+TSJol7M/wC37V6xMILF0N910WTxofp5URM7Gh49O4ObwueJ1vTuA+iLRe6S+VA5NvHGUDe+X6SpPnN5mRyGRcHOAl8gpuwYUUgnvxofT71gyH+aUsLjtX8toLM/T4RumRSKRYCsvQBF4o47cfwo3DJT/I/D19uT2l19j9cvP8vXCl3ji3fe46Z4ObPkkicM/X6b/tP8tF+UDYX58U1DOvNR8PusQ4RDbFwQ7SqWRHj3XY7e7cvTIBOriO1Hg27QK81NKUYaG/j/23jo8qnN7//6MZiaZuLuHJEiCu1OsUEqBUigttKVCqUFdaUtL3Y0qVSgUd3cLHiwJcXedyfjMfv/YIWFIKLTn9Jzz/r65r4sLmGf77NnP2mvd674JCwyiszkLXa2RYXc71pG7jQyny5CQVqWMy0joF0h8n4Bmg71Ta1dw6/ZC5HYBc8oROl1wJmdQ52u6tDr37En8ioWk/bqUzLfuJf6NpUiUary9h+DmlkxjYxbRUfNRqdr2+FCpAunZY1WbY55nstnYJ5saNxm/cpFzytZCcRFqJzZ3/xN7eAlIkTAJJSFNkvNX4p7zueypEdPdZUOTmz83Wo0sPLqQssYy3r93M54qz1brAjg5+aFShWA0FhEZ6eilI3NT4t0tkuH3PUxZVgYD7rhbHOj/OEK3u5GoPNBIJPSZEI1en0vaUfE6nD5zN8NjlsIeMfAhejgajYYhQ4Zc+zwBtfyKt+/U32HgnwcoIZ9/hrWyErnfFYqk6x7GkrqMHyNF/saSoiVMYhKNOh2N+jxOnLwdsOPlNZCuyT8iUcjwfbALliId6uS2O+v0vy3HuH4zxvWbUUZGohk4ALtg54dzPyC1SxlY0B2VrZSiQCv9Kt0RkCCXlLCn7Dy1Zk9OVcuYVpwG3Tx5zi+WxxIc7RaMWXVYmjJRDTvy8b7zit/AFbGAIMCphpayS9Kom0kaeTPjvnqQIo2JP/BnbVEJr/5iB8qo9VqG57RpbM3bitKqJnGdF97OHZCb9dxVtp9nh3XH7uNEqbSlNHnKXU9/qRSZ3U7okMEOx2lqEmiUX/Vb2lXTQL3Vxpw2vI7agqdCzvRAb34tqWZhbDDSG3BQvhpVhaKCrK7RgIu+CveItn2Q2vHPoz1A+X8IsVGh3PT4i+z58BU+fvFlXvjwPQZOjWP/75dw9VaRNPzaHSv/a/BVKvgkIYz7zuexpcqDsVe0GsrlGnp0X4WuMQM/39GMHqXgos5AQVYxAzxdHVLtMpmUQXc4TtLlJgvOMimuctk1g5PLuNL9NzC6AzqJBBBQ+ondO9ezkM8/cp49DY8AULE0jcGzuuHk5HvNwONG0cnDhQsGT6AOAEulHsVfJEX73tcZU149qg5t+8mUXpEmr7NY8Wi6rodKDrE+ez0AT+x5gp/G/NTm+nK5hr59dmCzGVEo2u6sSB45FkaObf6/4fwF8qdNQ7BYiNmzG0VgIGp1GP7+t1Bevp6kpO/Buze8XA1SWZuZm7Yw9a7ZFO35nlBFAwzaf83lSkpK2LX6ONp0N8ITvbn54Su6tMrOoQDur6vnN59AHo57GJvNRmxsLDKpHIlEhiDYkclaSjLKIA3KIE3rHTXBuVev5iybKkHk6UglUh7o8gDLTy9HbRO7bkKK1ayu345am0W9RUaHvgPpmjCRQg8fKBbb1OudJK0ypeoEbwwJXtjqTXhMEDuUvtiTxXvbMugV4cmPd8aj8HdB4aPmFecg/J0UjPZxQyaRUN1oIkvng6rp8FNOjaArTeW8puv+Yu8X2XZiH3GSLtRbe2EpOkL3U0uYp/qdc507k6DP4lJgIilFiUTEqFkZI5JmX/V0DGov6U1IgairFGfPNOgJVylbK9GWX4Q/ZoJCDfduE/9uQn8PDZ8XVJBvMBPp3HZG7VowNlrQ1hjxCdNw+mwaUgTiOna4/ort+EfQHqD8P4Y+PTtTeddjpP30Ee8tWMTzb76MtsbIwZWZaDydiO72r3lU/Cdxs68H3d2c+bao0iFAAdBo4tBoWgKPRI2aFcmOrYCCxQZSCZIr5M4P1GiZ0iQvvrl7LN3cXPgzWKuqEMxmFEFBRPcdQMPqVQhlZbgNHIRgt6PduhW5nx/OV9X3L8OlzyQkp8sRkOIXG/BXTl883mU/cWLDaobd8yBJN7VM5J/MuplNKXFIdh+lpyGK8rSTbfv3WM1gM4FT65bpGnk9XzZ+SeeCzg4+O5fxRmwwk8+I1yr+4HkKYn1RhgST5JtEtHs02fXZPJr0KDU1NXh6evJCZjG/lFTxeUI4t/qLE5BUqqTWKsVDEFql6K02O41mG+7qKzRMDh5EsIiBkXbnLrzumoFEIqNTx4/o1PGjlpVlLY8ug6EYs7kCN7dkh4AxJ/cz8vO/JCb6OUJDZ+LTYwpHtm8iMjOT0MTObV7vnTt3kl3oj59VIO/sVRL7t34Jqct5LHk6jwWI2jB1sScwGApQqfrSq+c6zOZqPD37trHltqEZNIgOZ04jUSqx0sK/mJM8hznJczhz5gwV5ZXERyTjrNZzetsG4voMILxzMsUZtVhO1bLAQ0DjpmFGdOtMnFQtx2dmR4fP1p0RCebH8mpxfqglMxHp7MTbV5RF1NIyXu0dz5asjjwZm0TS0ngMp08gValQJydDQwkTtizklqpMMkMmQbkdmX8nKnx9GZpyCGdfDQ0VVdRfGkpPRTKBSjtdnKWsVx7namQ0GohQO7Xy5Km12Np2O09dClWiZhBZuyChRYvn8vIN1+Aj/RmqmhVkXdm/KR07EroldbzOWu34p9AeoPw/iPFjh1JVUUnNlp/57KOveWzeQ+hqjOz44SLO7k4ERv//RyNlTqgfsy/kcVFnIFHjqOGQlpbG8uXL8fLyYu7cuchkLdkQc4mOis/PgF3Ab25ys6vw+fry5mVOVldQv3oHRp2W4bMfRqly3L65sFDsADIY8Hv6Kbzvuw+3DvHQQXzTrVu5ktKXXgYg+OOPcRs9ymH9urJS9m1cQWTnUPpNvRN3H8dgyFpjpGZ5Bkgl+NzTEelV2Ry7zcbxdasQBDs7v/vSIUBxVsqZbPiDSksgZqR4TGxDp8FQB4sHQH0h3LQQ+j/mMLzkwhJWXlrJyksriXSPpKtfV4fxbm4uhKuU5BvNTNq9hew5P6PbeojXN6QzpMM7rBofw+LFi9lYuZHgsDCWRIoEl4cu5jcHKN8XVfJipjghFgzu0iy+Z7XZmfDFIS6UNDC1RyjvTBaFsJSjb0F9Po2zlnoOHdvDuH69CYgWy5NZWVmsX7+eiIgIJk6ciEQiwWyuJuXYGGy2Rnx8hpPU5Zvm48/L+xJBMHMp83VCQ2ey79cfuHTkAMfXreSJ39YgkyvQai9itxtxdxePPTo6mj+MRvqlScgKVFKhNZJd0UjvSC+kgUkQ2NIObDSWcOr0dATBRl7+Yvr2aSGobvniQy7u303PCZMZNH0WxkYLDVUGfMNcxSDK2ABHvgD/RKSJE/g9bSUvrc7GquvAigcG0CvSBwqPk+wNJN8EgKVCz1w9k9sAAOxCSURBVODRM1GGumIx2djwWSpmQw4W3Wr0QP4LrxOR1EIyMpnKSUt/AZnMhY6J7yOVimXAZ0fH8/meLO7q48g50lZXseWLD5FIJIx/8hmOn5hIgL2e2bEaug5OxZSbS3laKd43DUYNkH8YqjORSCDW/0vMXhEU5W8ibURfYvpPwvzjFygsFpRu4n5KzVJ6OSuQJt3U6lbNaDS2KXPvoZBRdUVnTzO6TIW0DSBzgmhHG4Zqi7i8m+zPs6NtobJQh1wpxcPfmfLcbCxqL9xc//8n1/D/Ctq7eP4fxT2zbkfSbRTWY5v45bfVDJ+ZiH+kG5u/PEtd+bXbK//XYG1i7vte9RYlCAIFv/5KUHExNTU1WCyOrH1zoZZGi506q4D+XBUlmXWc2VnAYMt+JgvLuF/4go5p33Nqy3ouHtjDyoUvtdp32plNCAaxw6bx0OFW41LnlgeXVNM6jX9m+0byz57m4v711BaltxrXnyrHnN+AObce/WnHLqD0sgZKtWZ6T5yCXOnEsKl3Ubt8BdbaJlKvsR7J4Q/wkz9FiGocqh5tCMbVF4p/APa/32q4u1+LgFyYpnX5z1kmZV+8G9t/eYdH/vgZgC/3ZHGuuJ7PdmdhsdqorhY7f4oLCrgv2AeVVMI3HSOat3H8ik4s0xWt2DqTlYulYhv58hPiMV46VsYvb6exwdSfzLpK6ivK+e2Fec3rHD16lIaGBs6ePdvcNm63m7DZmr6jxmyH44+ImIMEBWrzREz6RnxCW7xUJBIpWl06x45P4MTJKWRmiq3O/fv3Z9GUnugfjuXB+5MY9+lBpn17lAHv7G51faRSJVKpWEJQq1u2bbNaSTuwVzz/dSux2+ysWHScP946wbLXUsSFDn8G+96GFXdD7n6WpuzHqu0EgoI7vzsGBSnw/Qjxz/73sFTqKf/4FBVfnKF2XRZSuQQXDyWCXde837ryMofjKyldSXX1XioqNlFc8js5ehMXdAaGJ/iz5uH+jIlz56en5vLB1HHknTnJxf27KbxwloLzqZzbuR1BaCL42i0IdjvH5r7NlhR3fn3jDKVZdRA3CjpNhoiBSG79DCfzEaLtuTwkLGXEiBG4Tb+fizFd2JXkhkUjxzvZiwu3R/H4bY6BsN5m55zWQHwbAUpoU4BcbLyKZBzQGR5PhUeOgdIx8D9Uq8NdLiNc/dfcxQGqirR4B2uQSiWYywuR+gRff6V2/GNoz6D8P4wnnp7Lm89XUrbxJ7b7+TLmob6sfu8kGz47w6RnerTtwPs/hmKTBVeZFN+r2vy027YTtmo1YUDt+HEoryKJWsPc2N1oxW6H+Cojlzafxm4VgAjuunc1FkstCZ2e5/ymhWirKuk7panN02qGLU9jy9/HWVkD2oFO5DlJeOfjr1sfXK8eND54L6F9+6Pp06/VcHSPPpzctA6AgNjWdWx1Jx8aj5VhN1hRd2qR7d6VVs59P4l+Pb/eN4aeoR78uCOV0/Yghu/8lZe/movUyY1UvwkkVazjOctsTn1yAJsgsOLBvnhrmuru/p1g1CKoL4Ihz7fa//Dw4RydfpT933/Lj/fMIq7PAMbPe85hGdWvE+gUnIXpJgWqD0u59UIFezIq8dEo+XDHKaJij2Ot9UFWnsy0UP9WnRPPRwXiJpcx3NsN1yvUQD2clXx4exIn82t5dJiYIakoENPrEqkHwfFdKU4/zfj5Lcfdo0cPCgoKHNR/VaogunX9FYOhkICACQ77DguZw8ZXD2AxpnNkyVTm/76B+P6DcfPxRSqTIdjNXGaKWqwtmjudPd3o7OmGwWxDZxLfxkvqja2un1LpQ5jXSioLqukYL7YsN2alUJiZx4BpM7l09ACD77wXu03A0NTFU1vW9HJwhfUBHmEMkkWSo85Eb4hi2f39wX6FOq7FiGCxQ1OAZ2swI5NJmfpiL+oqEkk/FIy7vzdJw4c4HJ+P9xDy87/BZtOh0wxn5LF0LILA3DA/Xo4OoiInm6pCkcS6+8dvuOXJFzizbSO62hoSBg4nNe0LbDYtFpuVV9ZfZOgVLdLHsqv5YWspA2Of4clJcVyovsAbvm4keQ3j2QFvIgUGDxnGO66h1FttLLg7jq5t+CkB/FhchdZm445ARy7U/hotb+eU4qW48UxIncXK0tIabvP3/NsE2aAYDyxWK2pdBZrO125Fb8c/D4nQloLVfxENDQ24u7tTX1+Pm9u/JlncDjCazLw17ylUtUWMePoNOoRHsuqdk2g8nbh1fjcUTn89DfqfxNeFFbydU9aqW6bx6FEKZonuwEHvv4/7OEe9iupiHb8vFFt5g+I8qC/X01hvxi/clSnP92xeTrDbERCQSpuuQ85e+Fmc6PQqKUd6eVH2kZLp6y+2IsT+sfAFCs6LpMEnl2/8t53z78cKeG61qM3y7uQuFOz9nrU1sRTZPQA41j0GvykdOJlfyx3fHGnWMQGY3juMRRPb5ldcC5/fMxWTXsx0tDqPL3pDZVP251XRbPFMYR23fnGIIEkFY1WHmSYdglOjmEFokwdzgzBozZzalo9fhBuxbWWE/iLsdhs/PPEg9eVluPn6c//n37dapqbmMDa7AR/vYW0Sns8X13O2qJ6JXYNRX1WCM2jN/PjsIex2AZlcykPPOvPrc0/TIacBTa2RsKFV7LZFM+D5JZiEIEqz60kcEISTuum9sK4A1F7gpKGyspLDhw+TkJDQ4sqc3aSd01TCMGbWisFsZ5/mY00/Usqun8RgZvJzPfCPaPuZeaZBz+iTImdjjI87SzpHYrNa2PPjN9SWljD20adw8WgJQATBzoGDvbFYRGG/+7Z/Ssb83lzYdAHv7gksPJ3PzjQx63fpjTG8eOhZtuZtBWDvlD14KDyQ2EBwkiGBawYLxUYzQ46lM9Hfk3c7tASeR+p03JGaTV93DZ8lhrV6QWkLVrvAgxfz2F+jZV+veIJUf+0FzGq28c0T+xk8LQ6zu47dbz5J4r3PMGbUoOuv3A7g3z9/t2dQ/h+HyknJY28u5LP589j64UJ8Fn3AuEeSWP3BKbZ/f4ExD3X+n9ZICVc5YbDbKTJZRCPAJrj07k3knASkBXtRelwCHAMU72AN4x5NwqA106FXACa9lYZqkQNwGQadmV0/pWHWW7n5kSRx4ghMAv/OUH6OjJimsk1SRLOS7JVw9b5+66NgtSORS6/6zIq1uhqFf9uT8OTuYhbC19WJ4Qn+7GvsSurOAorsHvgiwbW7uF73cE8y3xxLSZ2Be388TmaFjoeHRF/3mK7G8NkPc2HvTvpMuqP14N3rofAoxLTwBoI91MR4NrDY/A1hlhxqjQO5XGDbm1HBkA5/j4itdlXSf/K/rx1eKpUxY9HH1JYVExDddru1l1frzNdl6E+dJuDECRJun4JMKaNOb+Zkfi39Y3xQKWTIFFKcXOQYtBYCY9zBUEGwEISmVpy4iw95ETmsmiUvvs+Ul94jeUSo4z3k0VIW8vX1ZcKElgyQxWYh2zOQWI9YLodFqtjW7dwmvRU7NqTIMBva4Go0IdnNmV86R9JgtTGxiR8kkUpIvjURjWYiqqtaxSUSKd26/srec9tYsCOEKd1DyM44R0nDcSJ8YrglOZidaRVEeDsjk0oYGzmWrXlbCZcF8slz76CX96OfzYOOCd50eKhto71Ks4UZZ3PQyGW8eIWUfaPNxqNp+XR1deanLpE43YCLcJnJwvz0AvbVavmuY8RfDk4AqosbEewCPqGu7Dgkvtx069pOkP1voj2D8n8EOXnF/PbSk9gUzjz20Sdoi8xs+vIsHQcGMeiOuOu2y/63sKGijvsv5HGsT4Jjq6GhFt6JaP7v+SdL6ahRO5yHzWYjKysLX19fvLxat9Ke31/MvqUZAHQaHMzgaS1pd7vdTEn6XkaWe1KDgp5uLmzo7jh52m02ynOz8PAPo77KjF+4m0OwV7MiA/2pClQdPPG5p1Pz5/kzZ6FPScEpIYGoNasB0Fv0qOXqVt/D5Z+nRCJBaFKzvTLgsVgsCILQXOKy2wWqi3R4Bbo0+9cAFBQUcPr0abp3705Ik/iYYLe3GBA2Volv9EFdm1tI6yv1OKkVqDRXvb1ajKz5biMlqeI1fdDvXjapfiauwULunYkElloozqxl4O2xDn5QtbW1lJWVERcX50Bo/jOcrzqPTbCR5OvoFGzKykKiUDR7ENnNNiRSSatgsK6sFCeNBrWmdRfTn0Ewm8no0xdBL5ZkEtLTmPD5QVKLxCxS3ttiQKxvMNNYZxKFvYDyJSvQLt2AsfYMyt4mttf3As09SCRSOvQJYMSsa/s3XYm5u+ayv0hshz438xw1NTU0NjYSEhLicI+8cvAV1mSvobdnP7675WtsdoEtp4sJ8lKTHK5p5si0hezs95vdlAcNPEFt7THMlmqCAm9HKm15d60r12PUG1n6wqxmy4bLmTa70Ujh/Q+gP36ckK++pMbHkzE/ZzLC6EEHi/gdP/zlUId2fbsgsKKshoXZpUglsDI5xoEg+1NxFc9fKuJwnwQirm4vvga6Hr5AldnKj50jGf43zQIvHChm37JLPPDxID58+0NMl07x8q/L/ta2/q+iPYPSjr+FqIhgRs1/iV3vvcInL7zE8x+9x5DpHdjzazquXiq6jbq22dx/E9uq6kl0UbXWQVB7wvBXsF9czz2hc9l24hJJrmq29WgJMg4ePMieJon5J554Ag8PD4dNhCV64earpqHSQNKwECrzc/EMDEauVCKVKlHFDEFbfhGAM9oWYrFBZyZ1VyEBUe5EdO7AyndOUJ4rchjmLm5xtTVcFNPjlx2AQQw4jOdFAzJTmpia//bst3x6+lOkEimpd6e2rG8o5MTJ2zGbK+jRfSXu7o7kwoaGBr7++msaGxu57bbb6NKlC/uWZnDxoGgAd/SWJVTqK/lp1E9s2LCByspKTp8+zYIFC1jzzmvknj5Bz1smMej2O+CrfqArh+AecP8u8s5WselLsXw1+bke1LmV8vrR14n3iOXpPWvJKr4TZ8QApVP3N6hzc8PJYmaNvpLDq8X23Pxz1c3Xw2q18u2336LX65HJZLz8stj9ZDfbaNiRj9RZjuvgUIeJ7FzlOaZvFrlBDyU9xNzkuQDoT5wgf4aouhv0ztuoe42g4vPTYBPwfbBLs9li5rHDrP9AJL/e9c6n+EVEOVw/m83G2t2/4+HiyfB+Yx3GkMtR+Ptjzs1FGREhfo+W1n5DNqudk1vz8A7W0GNsBL4zJlPkFI7x4k+sqFHgLzHgfjmguOpV0G63k52djbe3N15eXtRtykF3oBj3myMp1BY2L3e8vIpndh0hvLyQHgo7M2bMaL6XdxeJ5N2UWpHE/fNPW6nOTUVpC2Or2x9EjI8hMfE9Sl9ZQN2KFbjfdhtBi95s2n8L8VSrTefc+YcBKCtbQ4/ufwBicLLs9RRsVjtuvrHUl2eQOLCla8aUmYn+uNg2XPzY48SdOU2kWw41ZjvQdhC6vqKOJ9ILucXPgzdjg1uVb9ZX1DHUy63N4MRoNaKStybTespldHNz/tvBCYgdPJ4BzsiVMhpLC5B4tS2g2I7/HNoDlP9D6NWtE9WznuDcD+/z/itv8sJbC9DWGDmyJhtXLxWxPf/1uv+/Gyn1jdzs20ZbdE0OOHujv3MNe08Wgl0gVWtwWMR+hWx2W4nCHHsG8jvzuDfmVlKW/tpMaJ23ZA37T+3i0eyn8QDmDF7HPaERzesd35THuT1FANz5Wh8a60wO2zVabLy6/gKlXnaed3GnJMEdN6MFN5UCiURC0KefkLp7NyE3i2/hh0vEycUuOE6A9fWnMZvFckFBwfd07vy5w3hlZSWNjSJ3JCUlhS5dutBQ1XINUitSGVIyhK/Of4V/UznJ2dkZi9FA7mmRhHt8/SoG3TZRzEgBFIuf11e2bKe+Qs+vxb9ytvIsZyvPMi2vBp+KP9jVu4gij3R8a2YSVXyO9Mhozpw9SmBMd0qz6ul9i2NAcPk7uNIvR3+mAt0BsQ1ZMNlwH90iK24VWkoWektLgNjcyQSYC4uQB+ugiYdjOFfVHKDUlZW2nEN5WasAZd2e5bxa8jYAT9UXMnNMi52ARCrF/funqSrcTFCXJwD44Z6eHMysZFTHFj2b1F2FZJ+qJPtUJQHR7oTGe9H9zj5g7kyPgvPUnPOgPleLU69AIvo7TnhHjhxhxw5R1v+RuY9gPCheh/pNuXzw6EvsXHUH4xvqeOjwfnIy1OSawjHJ86j6+GNeffVVAF7s9SIbcjbwQJcHEASBsqKzmKRGDksvManBn9Ky1SQmvkf9RjHjUb96dXOAEhX1JBrXRFw1nZDLnZFK1djtBtzdW7q7LCYbdptY3gzocDcz349HoWwJHFQJCXjcfjuG06cJee0xhP3vseSuCeg1obiawcPX2SHoBDhcpyPG2cmh2+tKpDUamB3Suny68MhCVlxaQYJXAivGr3AY6+HuwumGf6078bLEvd1uR1lfijpp8PVXasc/ivYA5f8YxowaREV5BVWbfuSTD77iiaceRlttZOdPF3F2VxIc17Zs+X8DdkGg0Ggm1rn1GxM/3wp1+Wg2PM4PczM4XlPB3GjHevHAgQPx9fXF398fz6uUK3VmHbO3z8ZkM7EoZRELK25vGhHI+OpFtrhmQdNzuIu8FLWsZXLzCmxpa1S5KBj3SBL556vp0EecuPZfquT34+Ib8DkXPTWZhbD+fHNZ4KJczk6bDdav524PD57q8RQ/XfiJ8dHjHY7R1/cmgoKmYrebiO/wZqtLEBkZydChQzGbzc1S70PviifjaBluHSQcPNsRrzwxy1FeXs4zzzyDc1Nr9NBZD5Jz6hj9br+Hep0S97vXQ2kqdBMzEx0HBWG3Cbh6q4jp7sfo4tGsy1qHh5MHwQ8+x+ivltI3v56HCifzxb5FKOw2ioMCCRjtRMen70cQcCh3yeVyZs+eTWlpqYOzsTLYFURxXlQxStj0JDYnT3bZeqB2duHrEV9jx86A4AHN67iOGEHQe+8icXLCbeRIBIsNS4UeqZMM16EtRMvk0eMQBAGPgEBie7fmmni4ttwTvh6Owbkg2Dmf+Tg2WyPFR1YTlrcA9aVIhia54tGzhTsS1tGL1F3id+0bekUZSemCPKArtu9S0ABszEEywLFl9bLzMoCAgNuIcHSHi/EYF42zq43Yunqw20ioyeG8SZSoP28LoINcdN++dOkSKUtT8MOPuH5xvLctg1NGbzoqiogVLAR0OEVU7+3ozVb8XnyJhpV/4PtYi+WATOaEQd+FrxeLLeRPPLERhcKOi0vLve4TImaGZAoJyYP8EOrq4Qr5/11793JQKqHLPbMoW3OQ/NpwhrvfyaIO3/DN3a3FC3VWG+sq6rgz0LvV2GUY7QIusta8k4PFoiFmWk1aqzGNTIb2bwizXcbl0mhMdz+y84pR2YyExbXNW2rHfw7tAcr/Qcy8ezIfV1RgO76Zn3724667JtFYb2LL4nPc9lR3vIL+XF31P402XUyVIoFVJ5Hwxp5pVBmqMFTfxmv9XgPEt3Xtpjw8UupwmeqP4GNDq72Ai0ssMpkahUyBq9IVk8FEB88ODB83B5fQSMLz3yBOe5DH9TKq/AZyQhfJjM9r2TFPS6y/OAF1GhRMWKIXajclCqUMlUaBd3CLDkpymAcR3s7kVeuJ8HamptFRw0GhaElpy+VyOvp05N3B77Y6RZlMTUL8omteF6lUyuDBjm95bt5qet4sZiEW+z7LOf8VVFXGM3TohObgBKDbmPF0GTGWpQtSsNbl0cvdjofQkT/UVUyPV+LvpKDryDCMxhKqq/fTL6g/Z2e2uBY7fTuF2ktv4vKdGWnT9xNhLSKm43jRrbgNSpO3tzfe3o4TkzJYQ9Br/UT+yMlvKU9dTbGTH965a8gtCsPz0UcYnNyPyh/Oo/BR4z4uColUgvt4MZgrrTdgtNiJHNva0E2hdKLXhMmtPs+py0EulTOs9xiWevnjrNYQHRKHIAic3nYKm1VD8shoNC5x1DecxskpAGW2GHyaU7Xk9P6YqKgnAAhN8GDOl0ORSFrbHkidFTjFeWK6VIvbqIhWxzFgwAC8vLzw9fXF19cXhoPb8Jbgh3u3Q20u7ybeiseOi+zPTWFQZD3P3iRq9uTk5DQvWl5ezpnCOo7bAjlrC+TkLV1RBD/L9iwdjy7bBig5/+PPuDg5PvIvXbrU/O+aGjtRUY5ZpnP7ijmxOQ8A04dz8CrMx33yJILeeAOA06dPA3D27FlKK8Vulz+q32f7xXKuhiAIvJpVgtkucG+IT6vxy/BRyCkxtnYjfrnvy6zOXM3diXe3GisxmfH7FxyH68r1WC12fEJdOZoqEmSTkm6ML9SOfw7tAcr/UTw2/yEWvVBBxeZf2Ornx+gH+7Pm/ZNs+PwMk5/tgYv7X/Ow+CcglUiIVjuR1thag4JZG6H4FA2+MVStEyerTTmbWgIUg5XGI2KKv2ZZOo3qXRQViY6/yuQzLC+r4ZWhv+EpVJDkm4REImH41DvRvic6rwbZbLxYdjcXDJ78Kk/n95+XMOee6fj4iA9WNx9RddbYaEGpljtkC/xcVex9WqzTV+tMbD5fxpC4lpR1165d8fDwwM3NrXl7ID7A09KeobRsNfHxiwgOmvqXr5nNZkAikSGRyDmTOgurVYurG7i7t36on045yPnIk5xWaSjILiXc5k3IoQKSajpSNjQZm83EseMTsFhqcFL6M2BAi1hdbe0RSop/5f4RGs5FdWFMyGScu3WAkBYysF6fS3HJ7/j5jsHdPfmax3xZQVcb1IshPX6kVuGOW8cGGpzccJZY2f5HGqq8RkyXalHFe6FqyvKdKy3ikcUfMOislvCbJzJ79s3X3MdlnCo/xcytMwFYNGAR42NbslaHV6zhxJZCrIZ9HF2dxNzvlmIylWOxWTiT/RqBJYP5xmsjo/POEhX1BGfPPUxl5TaCgqa2GUhKpBJ87+3U6vPLkMlkJCYkYLdf480/pDuEdMdq0qNxSaFY/Sm/l0NQmjf3dLqHvn370tjYSGBgIJGRkbx2iw8/H8lnulFK3XpRtC69c0tWp7TO0BxkX0bv3r2pra3F19eXiCauzTWPt76JPL1yVXOAMnLkSI4cOcKAAQPI32sh73wtuk4upNzdOmP1dm4Zv5ZW836HUEL+pMump7sLu2saeFUIcgj6BgQPcMikXYbJbmd/rZYZf5KVuR5aJO41FKy9hFGmIjqiXaTtv412Jdn/o5BKpTz12gs0+kRy9uePOXMxnXGPJCHYYePnqZiN125Z/E9iqLcrGyrqMNquIig6e0HsCII8Ivhg8Ac8kvwIG8ZsQN/UdSFRy3HpGwhyCV7T4zEaW/gIT2YUsrK8lmnnK0j2a/FvkUok2G9eQEGwipSu3shNHmRECCTIK7A11vD5544ckIyjpXz/5AG+enhPc4unIAjNnQ4AbnKBad2DCPVqyV5IJBKioqLw8fHBZDJhNosZFqu1jtIysasnPf0FAMxGK0Zd67fJy7DrLdj14nhu3lfs3deJPXsTKC5egUolPmCV8tb1/NK6RgJ338+7xa+xI/tpQCBfVsWa6Hq6Nb9l27DbRX6NyVzpsP7Jkw0cPHAX2RcGMHLqw6SUFPHZk885KL9mZCygoOA7TpycdM3jvxK13onUykWSY4OT+LdekFPh9XLzMsqQlkzVeycX8sCJbUxPO0T/959y3JjFCMumwUJfKDrRsg9jC3+lSFvkuIpJi810HDBjbjyOVKpErQ7FXOeLotMT7Bp8nhmDujBo4CnsdgtVVTsBKClZ3ub5CILA4awq0kob2hzXN9Tz3WOz+fSuSZzdtU28d67KFtqr8rC+Hs7ju57i9guix8/wMNFd193dnUmTJtGvnxgMxPq7svDWToSEtHC24ou+51ZFKoMUmcj0uzCZxMxGY52JlA05mGqlTJs2jcQ+ibyZ8iY78nc47D+xqy839/Bm0NAs3O/3xWv2PcSlHG0eT0pK4qGHHiKxYyK1wy7g9lgxT8/tib+bY1k2R2/ik/xynosMYEbQnwcS0wO9yNSbWF9Z96fLXcaPxVXUWWxMCWjb9PJGUFmow9VLhcpFQUNxPmb3AKQ30N7cjn8W7RmU/8NQOSl5/M03+HT+E2z/6A183nhf1Eh5/yTbvr3AzQ93RtpGLfg/iXuCffipuJoP8sp4sQ0zNICRESPJy8vj689EtdeZM2cSGRmJ54QYPCeIHjXxptcpdUvC22cYg4pc+LW0mpg2nE49Oz6IJm4qLg2peI7vw+M2Kb/8kk9BQQFTpzpmNEqz65v/rW8w81FRKV/lljD04EaeHDoI3/BIfn1hHggC09/8gMAYRzXZ8vJyvvnmG2w2G3fddRfR0dGEhz1IWdlaEhLeprHOxLKFKZgarQycGkuXK/gVIHqzVHx2GsFix2t6PFXmnc1j2Tlv07fzXgq/3YKyJpi6+mw8xosaKVkVOm7+dD/HZVqR/wEYZAasQjm3fVbKpLUPANCwoYTQ888hdG0gasw9DvtOT88FQKv1xdt7IHlnRA+csuzM5mVcNB2oqT3U5nd2NQS7QMbyLPo3GiiLkNCvqo68iDwG2bdQr4CnjHqOqqzcs+sSdw2PYea5HOrMMrp4AdmXz/kjIsLnIJOpoPQMZGwWB369DZ4rAGBY2DAWDViESq5iRNgI1pbXIpdIGOfnQf+pU2moqiTn5CGSR4ldPdXFOla8eRxBgOQRc0jq29JqHhv7BtXV24iOerLNc9pwtpTHloklkG/v7sFNiY48l7qyEnTVVQgIvH/kPfILP6CxaBwvj5vEhGQxuDz66wv0U4gB7HzTWhISjhHmFkZto5m86kbivVw4tj4XNx81sT015Jw6TmTXbhTXfEJARjonGyfjITPjgZnc3CfJzYUhg89z8I9ssk5WcGJTHnO+GMIXZ75gffZ6VlxawZ7b9+CjFjN72n2FyLMa8CQBv8fvRNnEv0pJSeHs2bOMHDmSwsJCdh16j5UBYsnpQvUF3h74tsO5flNUiZdCxoOh19fI6eeh4WZfd+ZezMdJImV0WyT5Jhyu1fFmdimzgn2IbUMq/0ZxmSALIK0twSm263XWaMd/Au0Byv9xeHm4MuvVN/jlhfn88trLPPLBR4x5oDMbP09l39IMhsyI/69qpEQ7q3gmMoA3c0rxUcp5IMS3zeO57AkDUFJSQmSkIyfBycmfiAixjfL9eHg1JghNk/S63S44lGgUCg+8vUVuh1QK9957b5vH1mNsBEgkBMW64+qn5pOL1dgVTmwZOolOX7/MiPsehqY34uwTKW0GKJc7WtLS0oiOjiYm5hliYsQyU2lWHaZGMTOTdri0VYBirTSIEuiA4WI1cWMXcPHiUyiVPnTpvBhqZDjViJonxqy65vVK6w2YrALzbA/xtdOnnHWSE9JxMwkueoInPoFELj4W9CfLcbKEIhwQSOtqJNJHhrNSjt0ucFadzB5TA+91tFO3YhnDvS5w3qZE3aUfRosNlUJGbMwLhATfiUrlKH/fFhqqjVxKKWcYwHmYu3gMZnMV+sZ+5J/2wqgS1WyXHMrDo7MPmXoTsoYpLI6JIz3ahWn93oa8z6mo2ETfPjshMBnix0H2HmwTf8NeVoYiIEA0wmsiI2+vquehi6LU++M6f56PCuSW+U8BYjamvr6e7Jwc7IIdCVKHTNbnpz/n67NfM6ixD1PXPE5EVBS2AR1RqUMIDBSdoa1XZP0sV2cAgcCYDvSbPJ31x09wOrQpcxH6LePWvII9soCGfZXoDT3IunQSD6uOx5Ke4aJFx602O+M+O0hxnYExMmc6VYv3WOqOC1QXbAOJQO9HatH71xKYU04p/sgCr2x1t+Phf4XsvERCkm8ya9P3IZE34CJXU1a2HidVIE5h4c2lUqnSSsmLL2JpaGBLE5doyZIl+Pr6Msl6nA12T0xSaasyjMluZ1lpNY+H+6O+gRceiUTCBx1C2VRZz6zzuSyMCWZmsHez0SSIYm7fF1XxXm4Zvd1deDXm77cEC4JAVaGOLsNCKK+oxsXcgH90G+ab7fiPoz1AaQcRYYHc/NQrbHvnRT578WWe++h9ht4Vz66f0nD1VosT8X8Rj4T5UWG2sCCrBI1Mxp1tpIiTkpIwGo04OzuTnJzcatxut3Fx/x7Urm5Ed+/VHJwcyqrizu9EA7czr9yEQmFh/t75nCw/ybKblxHreW1lU42niiHTW4KOOaG+fFFYydSC8zy0+GeUajV15aWoXDRtkjUTExOpqqpCLpczYEDr2npAtDsDbo/F1Gih2+jWOjWqBC/cx0SIxzIwBIlU0uyoazabqbBU4DG9A4LWgqZ3i1LngBgfFk3sjMXWEUmfV4jWplLbVIZpMGwHHuXkpnUUlB4j1qM7m3tE8+WnYgdFxhujKakzsjNbi8JmJezt1yi1mlDE3MTm2E5sLw6Cl7eSNyEPSd+5ODtHXPP6XQk3bxUJ/QLJOlXBmAdFqX6l0ofMFBMHV2RwEzI8fdWMuzWOHv4ebKqsI6+sCrMhlp3A3WovLJYagoKa1HAVKrjjN6zV1eSMvRlbfT3eDz6I37wnmvfpqWh5/IVdxYmw2+189913aLVaCIC7b51LZJKYVTAXFbHz7B8gCAyqaGAQxyDnGKU6Jy7Gu2K3mwgOnsbErsFonOTk2CwsFQy412rp73kFJ+T553Hadoiywc9jM6QhUxehk99Nl349OLQ/A91hI/H0JC/Oznp7GbYxo9nRKQqrTWgmXp8zGenU1G6m1jQZBwoSevRYibbDBW453shL57Pxdo0lOmYgZmMo4ESPm8PxipYSHhNMVUEuhedDaMwSfY9qKtaTniEScf1j3kIzrwfB8kBSlu3nldpIArSV3OTSQJ7Kif79+2MLCGbRRScerdnGqPJCAmaOc7iW57UGTHaBIZ43LpbnoZCT0ieBO8/m8EpWMe/lldLTTYOXUkalyUpKfSMWwc59Ib68GBXoELz8VehqTRgbLfiEaDh1+gIAHTsn/O3ttePfh/YApR0AdEtOoOLe+Zz97l3ef+UNXnz7NbQ1RlLW5+Dq5USHPoHX38g/BIlEQryLSErtdg3DsfOlOr7OUDC2kzfJbYynH9zHtq8+BmD4vXNIHiUSKvdmtLgIpxbVI3FOa9Ylee7Ac6y6ZVWb+zOcv0DVZ5/hOvIm3G+7DYlEwssxwbwcEwxXHMHgGW1nX0Ds4Bk2bNg1xyUSCUnDQjEajZjMRuQKx+4qiVSC6+DWLsQAv/zyC4WFYvvrZc2MK7c7vXdLt4ibWxLxHd7AbKkhPGw2IIqcFWsvkKNNpaznG83Lnq+4SNeATtzWLZgtZ4oQnJwoj+pCTOJkKmgEmgif216AvnP58VAuOVWNzL8pDg/nliCgtN7APUuOk16mZd/TQwj3dmHY3QkMu9txYtB4tqTtF87oSkgHkSC7vWcH9rp58tHOTKZ0D6Ffj70IghkbbmRVaIn21SCRSLBWVWOrF0txDZs3OwQoPd1d2NcrHrlEzNRdCUEQHPRaopJbeDwFs+5hxtACutNArsQLk6DACQtlfmKg4OQU0HydR3YMoMeRCxQZLeysbmBrgpqV507gE9AP74JSuhurGZ99DJPXk+xNcMHuq8YAEBUKh0SRwN5zH2aAr+N9/9v9vTldUMfUnqE4SSTIZBKs5j4UXhxOaGIn0iyQZu3GiO5WRp86garCyqEDwWRmbgG2kJyczJkzZ5Bra1EXiTWy0IDxFKpDkMnE+6zcIuHpfW9hsVu53ziVrKJeZLsFku0WyBMj/Zg1TPS0uv98Hrv9erDbrwc3D219P66vqMNbIaeza9u/3WshXO3Ewd4JZNQ2sKWghNM2K/kGG14KGfMj/LnFz4PwG1Sa/TM0E2RDXcneewmLRE6nxPYMyv8C2gOUdjRj9E0DqKqopHz993z07ufMf/ZRtDVGdv+cjrOHE6Hxf5+E9q9ieVkNI7zdSNCo2xz/YHsGBzKr2H+pkqk9Wz8k1W4tdWzXKzpnZvaLIL9aT4yfhkGxPhisPRgQPIAzFWd4d1Dr1l+AiooK0uY9gV9hEbp9+3CfOJE2+2oRuRWCyYZU/fd+ag0NDXz55ZcYjUZGjRpF3759b2i9+voWfky12Yq30nH/gsWGKa8BZZgbUicZwcHTHMYHTLubo6t+J3HgUPy6JbCv/A8MytPM2lHIuZnneOu2zvSO9CJFMhV53gnC7CZekqpZX36eiPJN8MZn5FTqeHWDOMn+fCS/WQcGYF9GJell4sTw+e4s3pviKGV/GeGdvLnztT4oVLLmzjKb1Y7NYmdIBz8H3x9BcGbUuzvIqbUQ5qli/7PDUXWII+idt7GUluF1z6xW2+9wDd6CTCbjvvvuo6ioiMTERIxZddQsTUPurUaqdmaP5Q4mCV9zSXonVcYoPglYivTcGG4a/DxOKsdgfoyPO0uyjxJx4Vt+3NyFj12+oUoqp+vsrTx/Yjv3jJ3AxE4d2V1Vzlu5dfT3csUt2gu31/ohkUuQtFEW6RbmSXKoO3sK9xDgHEBHn44UCzI2+4Qz3AoTTmVitAs4CXYeO3cGgMYOXQmSVuJl06LYXkG4Xk99uBX/PuVUp3kwOkrNiPF9EFTOJCX/Toa2DEuZSNbOUOYyhv7swkIj0Hlgt+ZjGeLlyoYmQqt7nOMzwmoXWFNRy0R/DxR/wfPLptNRu3QZDRs3Ys/MZJQgMEouR92xI+4TJ+Jx20Qkyr/ut9MWqop0qFwUaDydqCnMwabxQyFvnxr/F9D+LbTDATPunMgn5eVYUzayZIk/M2dOobHWxNbF57jt6e4Oeh//KVjtAqlaPS9Fta4zn9qygXO7t9Kj9x0cAFxVbd/Skcndufu9z1GqVLj7tSiBhng6OwhKOSuc+WrEV396PJcuXSLfPwC/wiKEP3mQCYJA1XfnMOXUo0r0xufuRGxWC1u//JiitPPc9tyreAcEYa2sQhnSdktjfX09RqPYZn327NlWAUqJrgSdRUecp6Oo1J133smHh4/zh9qHxYfOUzokyYG7U7PiEoZzoiR9yGOuEJTssL5zaCxdZj9DXFNb6pjOsK6gkBgP8c1y8d4cPtp5iRCDMxOt9Wwo+BIn10n4KaPoPGcedLuJALOVSB8XcqsamTfC8fhGdgxg07lSKrUmnhsjCrdZysuRaTRIXVoyRXaDgbrH7sWQmkrwJ5+gGjyM5W8co6HKSO9bIulxhf6J1WansNYIyCiobWlNd58wAW11FUfXryKqW08ComObvx/BZEN6jXvmSt2WhhNl2PVWzHotF5OS2Ow+mI8q4XZ7ImYJzKq6F3t0NYFJP+KqaRKiM9TBmodYWFeAX+ebOJgdw1Bv0ebAXWfnnc/epqMlC9W8p3nl8CuszVpLnGccr/Vqytpdx2l8bdZaFhxeAMCXw7/k9VI/zukMIl9LKmBEgq9g51Kf6TQExNBABY+knMFFWkmqhxR5kZnkUQew+YNfUg19fcawdNVytqjPU1cdzNy77uTtgW9jspm4JXQcW7dlE5dXyRNjElApZNjtFs5feIyAyu2c6vIzgd79WvHD1lXUUmG2/qUOG/2pUxTPm4+tthbX0aPwvGsGMnd3rJWVNB46TNnrr1P722+EfPZpsw3Bv4LKApEgK5FIsFcVowxuz578r6A9QGlHKzz6xAO89XIl1dt+ZZO/L6MfGMSaD06x4bNUJj/b3SHt/p+AwW7HZBfwu0pkSrDb0W96mZk+uVSdWM2ji0r+1JnZNyzi33I8Xbp0IWfkTZwceRO333FHi+He1bAJmPLFFlPjRZHEW56TRfqhfQAse+UZxtVZMGVk4NyjB+G//tKy7oklcPRLQgY/y9ixYzEYDPSKjnYw+CvVlTJh7QSMNiPT4qfxQu8Xmlf39/enPq4T+oq6Ng/NfqXz7TeDIWk6TBQDM63RwogP91HTaGZ0xwC+mNCJh/aM5S7rYPz7ip1Abk0ZoSJ1CKNf+4KCvRXknNEiAY5tgp7jwVkpZ/u8QZit9lYCYV4uSn65r3fz/7U7d1L0iKhyGrVlM05NJGdTVjaGVNGfqPippwjYcYSGKjH4OLYhl66jAtDr89Bo4lHIZcyK0nMsv4FbEj2x2gX21mrp4KLi5A+LyT5xlCMrlzL/9w1IJBLq1mTReKwMmZeKwGd6trpGgk1Af6ocmbsTmj6BmAu0mJRGLh05jVuXm3k/YDz6aiurI5wpcZHzYEgUr3lfEWhe2kbDxRNkGIdwm7yQ7GgLu7OTiPMoRMgR6JouZpfq1qzliPSIuEqtKJwm2AV0h4oRzHZcB4cgkUtZWlpNidHCw2F+OMukyCQtAYxMKiPQScE5nYEn8n/mubzv0boEMyrkPdYli4JjY8+oyTGKLclbMHMx0caiC9X4+5/FKkDj0VJ84n+m6OQoPLCw9cP3mTlhAq7Dx1Df0MCl7C0kNTQQpRa/m8bGTCort1OJL/VnH+G2Yacdrp/eZmdBVgm3+HmQdIPlHcOZMxTcex+qTh2JWLYURZDjS4nXnXdiTE+neN588mbcRcTvv18zuL9RVBXqiO7uR4NWj4uhBs/Iv+4I3o5/Bu2N3u1oBalUytMLnqPRN4aLv3zGyXMXGDc3CYkUNn5+9k9t3f8JKKUSZBKoszgKWkmkUroEipOVj0r/p8HJv4qaRjOHsqqw2uzY1M5MuvNOZtx9d7OLcFuoMldTOVKCuocfAU+LWRrfiCjCu4gtjBOeeBZzkxqo/sQJx5V3LICqS0hW3UevXr3osP8AeSNuIj2xRc5fa9FitInnn1GT0Wr/78SF8H50MDsbXdDuLXTQ2PCa2gGP4W4EODW1D1dcaB7Tm23U6UUS5oHMSux6K5jtuNjVmHLE0tGsfhEsf6APR54fRsf4cG66txsBUaJ2ycjZLceokElbBSdtwZTZ0p7csP00gl08VlViAk7TplHUqSsln/yEZ4ALA6fG0WVYCPe+P4CTJ6dy7Pg4Dh4SM0sv3D+VPxbM4r47p/BxfjkzzubQ88hFlL6tfaaM2XUA2GraEAIEGk+WUbsqk6ofzmOrNRH4TE/8ZycTnNiRO7d8xnsebgzu5kaJi3h+Xxc5asXUeSezvWE+x3TTWXtqBq/N/hwvfzXLlZPZFjoWSVOmqLFjIgv6LGBs5FhmJc5i5aWVGLNrqd+US8OOfKp+OE+azsD89ELezytjYEoaPx/J4+zZKOYVfcqclE9IOezGk3otv6SfYG61SGh2bSxmac7TXDh8C65WLeeCW4z08uXibyleWsDaI0qWGybiMiAIb108Pj75aLRahu/aTfGjj1E4ezaFhYU0NIjB9sGD4vZVqlBMMn98qeQ3yz3sq9E6nH+91UqVxcpk/xuzz7AbDBTPfxJVQgJh33/fKji5DFV8POG//YrUyYmS555t02frRmFstKCtMeIbquH02TSkCMQldLj+iu34j6A9g9KONqFUKnjizdf5eP48dn28CO+F74kaKe+dYsvX5xj3SBIy+X8mvnWSSklwUZOqbW0G5nbHV3DwI+hxTxtr/nsgCAKTvjpMbpVozGccFYynyc6mfCkuJju+szsjdXaU2bbYLdyx8Q4qDBW4Kl057C0SbxVKJya/uLB5Od2XX6A/dgyvu69Seu0+Ew5/Cr0fEvfZ5IB8JeI84/h82OdUG6uZED2h1bi7Qs6txRbqDpbRAGAT/V4AZK5KNDclQeTX0FACSS0cFH83FT/M6klWhY4ZfcJRKGR4z0jA1mDGpbdYHqtfvQa/pUvRPDIXhg5FrpQx6RlH75X0jAUUF/9KTMzzzeTbq2G325FKpXjedTeWKh3GDCmmXF+qf76Iz6yO2Pa+w2eqTM4Nr8D/6K8IAUGMGBrS9L3YMRhFIrC5SUhOIpE0B43mKwTzutx+Fz2HjsAzMLi5DOF5Wyz64yW42FZx8bEn0Cob8H7gfmLixDZvmUvLdyp1Fbep1rgydYGjxseHpdWk6QzMiwhw+Dw1uwxDpR2a6E8aZ1fuvvtuqqurSe7SBanVysqNG7m4ciUAw2cN58l9oqZKVtQl7lINQTDacO7qh59SgbdCTrXFykg3Da/8ep5Ys5Rb9SIvp2JPKVmnPyCxJp+LneOIjHclzx5LT2EZEglsz55LnkcjLsjZIRnLDLWcvsbDpFdo2D7qRepdolhceYB5+bvp4+yG8Og6srfvAIsFqZs7ISZXgnwLKHLyZFeYDfnaAyRfzMGpjyj89pDsM44f78TgUS2kb1+FAglQab6xF5q61auxlJcTtuQHpE5/Tn6Ve3kR8OqrFN5/P/ojR3Dp11q59kZQVSR2PvmEurJvYxp2JHRLbpe4/19Be4DSjmvCw92Ve15byE/PP8nS11/h4fc/ZuxDnVn/6Rn2/prOsJkJ/zGNlJ7uLmytqsdqF5BfmSmJHir+uQbqt+Wh3VOIZlAIHm34tVwNW10dpuxs1F27NpdSBAEaDI5qrsPKrShzTViA+h35zYJwzRDAYBUdgbVmxzfLK6EZOBDNwIGtB0YupGHI0wiCgDvg8/JT6NZuxf2WWxwWGxz6546rypCW1k5VQhsKnjHD21zvagKqupOjd0r5W29h1+komvMwCelp1K1di/7ECXwffhhFUBB2u5ni4l8BSM96lwX1w9ha1cDvSVEM8XIDq4ldO3dw4OgJQkJCmD17Nj5zHqH845NgFZoVY21pWzjsY0IvN1DuvJ1gzwXNxyCRSElOWkJNzUGCgu9odQ5PRgbQwUVFJ1dnQpxVEO74/auiPVAZ91L3/hdIjnjihpz66u9giRigqDv64PdoV6RqOXKv1mVNwS6gO1zCeAGm9Q9u5dqbkJBAniq1+f8lWXVEJkQSERJC7u1TMaWlUTWtJTAM1rSUKrqGdifwuV5gF5A6K3ABUvokoLfZUQtwwDOb/BoDQoAKe7WJLSoLfq7+JNbk468ZjdnSiSAA+TIAUpzB1w+0t1vpJWxAgkD2vgDqal1p9IwCAUaUbENirEdhrAd5HVGrV2GtqcW5V09KPnyFqOR9/Ch5mxxzLBvd4XhDBPLC/tS4p5ORPpDY2BatFQCZBBQSCUZ7aw2YttCwZQuaQYNQhrduqW8LLgP6o4yIoGHLlr8foBRqkSukePg7U5Gbg0XtjcalbSJ+O/7zaA9Q2vGnCA8JZPzTC9jy1gt88dKLPPvRBwyfmcCOHy6i8VbRe3zU9Tfyb8D0QC+WFFexoqyG6deRyr6MuvIytAcKkCBBt7/ougGKYLeTO3UqlvwCpG5udDgm6qNIpRJ+f6APKbk13NQ5gCXlNYT6C8irSrBWGnAd1FqITCFT8NOYnzhfdZ7RkaP/8vleKt/LT0cf4bBOYGbcWEK1q6EfDO303F/ajjLUlaDXm8z4/o0ZL/eJE6n95Rc8pkzGptVS+vwLIAjUr1xFQnoaUqmSqMh5FJcsQxn6OluzxfLAHak5lPWPg6/6cbF2EOBFUVERp0+fpmvXrgQ82QPBakfR1FbrNOY1RuxdyHoMdPJKIiHQzfE43JOv6fPjJJUy6XrkzOBuqIPcQS6AVULQVEfJfOWfkMKNl2qp3yiW6MxFWrynxTuM+/n5MebVWWz+MhWFk5yAaDGVYq2txZiWRpHGF0PmFlzHT+bOoXcS4BPAjsmiYFuNsQarwo5S1lJC1MhlaOQyLlVl8XCEM17dIxk9IhKr1cbQomo6BI9EKCzAVCmnYU0+Umdzc9e3saqGgYU66lwVnOvUZCOQaGDk0O/oUCgQOiSILglzoe48dvcw8pw1nNKnsrNhJ4d+vo/nfCJIqjAyQn2Ab9xFkrEEKVFps6mIXMKw4dPo0b0bV6LYZMEsCAQ73Vi3jeliGj4Pz2k9sP1lMZvY+XaY9G3zxxKJBHWP7hgvtnY3vlFUFmrxDtEglUowVxQi972+qGA7/nNoD1DacV107dKBqvuf4vTXb/H+y6/z0rtvoK0xcnRtDq5eKhL7/30VxxtFZ1dnbg/w5JWsYhI1apKvoYdyGXa7jWUvP0WQPZJ4996ETG9NgmxjJWy1deI/Gxz9U2L9XZuN1p6LEttIbfGBFNbokV7DWDHWMxalPYhzhQZ6RqjazDbp9bmYzdW4u3dvHs+uyeTlbU8RJ/XjxYAClp26wPRmvTgbV/5sy3OyMOp0hHVOumY267IZ31+B2WpnZ1o5HYPcCPdu7W5tfnQGW0apuCn8JgKcnVF36YIhNRX3yS2+O4bi20j9NRGlm4IpszzZU6Plm7gg0FdDbS5DUbASsfV43bp1hKel4dK3bzNB1i7Y2SY1cdu493nNNwmJTo/h7FlUnTq1IibXGmtxVboil/7FR5pHGIoXTkLAo5xSGTlxTM7wrMWMe+Sh664q91KBQgoWOxV+8OKFPEb7uHPrFZwLv3A3Zr3jmCFT+Pmxdc6bfFoq3jca/UscOHyAtbeuJcAlgFcOvcKarDUAnJt5DgBTXj0HVy9nW/FvbOlSA4BXwROsWt2NkRf+IPv8BebE3oWmvpCbKnbh362SwJ5VqL1/5PddedRUW7hgOYFZqGJrZRneNU64lvTh0KFybBY7+YfKSFo8jEPeczi6ahnaXU+wamARPlYPnCRO5NXomNho5XV+YsKFA1QWK6l9dSi1pv0E+6jo1dOxvAewubIOmQT6eFzfHV0QBOx6PVI3t9aDqWIWiHMrHAIUAJmrG/bGxutu/1qoKtQRGOOB2WzBWVeBJqnP395WO/79aA9Q2nFDuGlYXyrL76N07bd8+NYnPPnCE+hqTOz9LQMXDyfCO/59J9EbxaLYELL1JiadyeLl6CCmBXrh9CcKkhKplKz602RpT/Nk93HXXK55ebmc8J9+RH/iJO63TkCw28k5fRx3X3982ugAeuz302w6K8qAX6nxcRmVWhOjPz6AwWJjSAdffrynFwBndhZwaGUWnYY5Yfebjd1uxs9vLJ07fQbAt8e/5qJg4qKtkhHH5hGhL8fZFEBNcE9qa3V4e4sTW01JEb+9MB9BsBPffzA3P/b0dc8RwKjTsenTd6ksyGP6wvdx823tj/LBjgy+3idmB06+NAJvjWMQtvDoQo6WHmXJhSWcm3mO8GVLsesNyDQtk1HWKVEEz9xg4dOEMFasWMH2nSu5EBzM/RO+pFPeAQx1UvbkudO7Lo/y10VuTnzaRSQSCWsy1/DqkVcB+GjQB0Q89AGW/ALk/v7E7N2DTbAhl8pZk7mGVw6/AsDR6UdxaRK0s9n05OR+glLpQ1jo7FYB3JmKM2zI3sBNxWCJ3YuQFoS38SynigIYZbFQX1/P2rVr0Wg0TJkyBZnMMdBT+DmTE3WJs9s3Y/rGwKb7XmZtRR1jfN1xkkopy7rEzu+/JCCmA8PvedAhqCoOjIbSJrNCQcGYyDEtY7pih/2Y8uqpWHwGVe8VjEouYlRlByr2J+NGFS8MP4V/eTHp3j0psqrBJY4gTQmcgsCeVaSUSClvqAcF/KEaC8GFDFQsxwhU1aTj7G5GWyNHKqmgOCON7BNH0UvV7NDchHDJh4/s3hxVnqZMWse7DOZVPiLbP55qpYRBfZ9DqnwClZMj9wagxmLl84IKJvp54qG4/jQjkUiQeXpirahsPTjyTTj6JQxs7XlkrahA5nljJNxW65pt1Jbp6TwkhHMXM5ELNqLj2wmy/0toD1DaccOYPm0Cn1dUYDq8ju+/8+fee+9AV2tk2zfnmfhkN3zDblzK+u9AI5exPCmaBVnFPHepiHdzS+nrocFXqWCCnwd9PVrS8VKpjOkL36csJ5Oobr2uu+13jr3Dr2m/cmvMrSy8S5woT2/dwO4logFhW2Z/GWXX5paA6L9ibvJgKakzNH9+do84MWWkFBJ9s0ggvOwyCzA8ciSbyrYB4KwL5uYTB1EXnMGDI3x2R3mzMqwgCAiIHQx2m2OHE4C2xkhRei1RyT44XUHizUs9SV7qKQB2fvcFtz3/Wqt1Jfw5tyjGI4ajpS2uthKp1CE4MaSmknD4A9w0nfCaPl3cb14eAMXFxTBtLsUrPqeuNgLvxjNk0Z2rwyRnRUuWzFmmwlYltmpby8uZtXUWpypO8WCXB6k2tvgw1RhqmgOUktKVFBR8J14ru5WICMfywUuHXiK/IR+JTsuL52o5WDkMq58HYOTkyZPU19dTVCR+V9nZ2cTFOWq5lGVnotNVY7KL321QRRn5weHIJRLsgsBvf/wOOVmU52TR57apaDxbyk3PjYlHW3gJRW4K7tl9efDeB8UBk46PJAGk+AwguOstNDScw4kwBKkFo6toeIhvBiVVUn5PqsKubWRdT+iV2RWauKhqoQG1r4GLv0VT77oT3H2QyqVs7hRCN/cCXDDg4mHAVO9Ev5s8WfveeyDo+f2VXzFNfJbvpU3ZQzss1pfSVdFyL9TVDaZnXQ/kQV1Rmp2Qu3pwNfQ2O7PP52EVBJ6PunEFanVSEo2HD+P7yFzHgaSp4p+rIFitNB5LwX3c+Bvex5WoLmlEsAv4hrqy7aB4L3fr2ulvbasd/wzaA5R2/CU8/Oh9vF1ZQe3OZaz39+Pm2UNZ++EpNn6RyuRne+DaBpnw3wmNXMYH8WE8GOrHH2U1nG7Qc7y+kUO1Wvb1ikd6xVuym69fm9mBtrAuex0gil8t7C8GKFd2L56tOItPZBQKWctE//HUZNanlnB7j7br1kEeapY/0IfCWj3ju7SUwXqMjSBlXQ5Jw/sS2W0pJlMZ/n4tGZheaQaWf+lPXlgPsjtqcJNbaYs94x0cyrTX38PYqCUyuXWKfcOnZ6gt07P7Z5i7uKW7IqxzMsHxiZRcSmfIzAfaPPb5N8WRFOJOxyD3VtkTgGd6PsMd8Xc0EztPlJ3ii9wMBgT15t6wSCo//wLrmRP4c4L4b59BIpFw2223cfHixSaxOQGD9QESPGJI8OjPqrLjuN/sjf8rm5ozHaMjRuPv7I+nypNI90gMPy5Bf+Ik9puHcGqLOCl9ffZrdk7eiVwip0dAD0LdWlSE3Vw7N//by7s1ETnJN4n8hnw6WSRIJJAgySbVGoAgV9CxY0fq6uo4dUoM5MKvIG5aTDaOb9zDkRUfA9DBK4CAQ8cYm/ocscePIZNIOFanY1VgHJPOHKPR1RO1q2PpwtfViZ65W2iorIArkwYpi3E7/Dk3AamNGzju40R09DMEzp6KtG4RhWc2ocrsB6zDKmshnybVdCAUBY1Rp1g+eByrVKHMXvohbnTEs1wMrCxdlZynCxa7HLnEiktxby6dLSS2VzKZKYfpOHQGr+Wam7fZ3/cotytXUlwdi7rYDZlOy5HSaiLKmoK+p0+2uqYNVht3nc3hnM7A0i5RBKtuXO3V/dZbKX7iCfTHj+Pc8/ol2brVq7FVVuF+a+sOthtBVaEWiVSCd7ALJdlZmJXu+Pl4/K1tteOfgUT4V5rI/wE0NDTg7u5OfX09bm3VI9vxX4fFauXNec+iqsxh4OML6NoxkVXvnkCmkHHbU91QuSiuv5F/I07UNzLuVCZvxgZzX4jv9VdoA2uz1vJ7+u88lPQQQ0KHACJpNvvkMRZcfJuziH4llzkB/yRyp9yO8Zy4n7wDRwjR1hO4Zxfm5CT22KVkbttMoJ8vs++7r1XZ4Uosf/MYVYViG+WVAQqIuinFumKGhA5BKvlr5Nkai5WnMwoRBPg8MRy1VELXNS9R5jkFAE9jPZsbqzC89BKFcQkMXvMHqjaOs/yHA1hEXTLW5L3F3GfvQtp1Wqvl2sKvF3/lWNkxnu75NKGubfsRAVitOiQSBTJZ6yBLEASqjdV4WyxIDn4Ezl5iW7fa40/3ve3b82Qc3otFvxWAPmGxeG0Q/93h5AmkLi5Um62MPJFBscnCR/GhTAtsXQLd8NHbXDp6EGd3D+Z8I3Y8kbYRlt8JwJEeHuid5YSHPUBMzLMA2Kw2fvhmOT2/XIRBaed4nIx90fcwrDoZgNQYJ9Z3FzNIt7irGLilhAaxE5sBB55g7ahxOLv3oGNJGml1ojjcnG//4OcXUrBZ7ZxX2Uh1F5hqktF52FwEudi9duZr0SPJv76R7nllACSkO5JT6y1WppzJJs9oYmmXaHq4X597ciUEu538adMxpKYS8ccK1J07X3NZ48WL5M+4C9dRowh6a9Ff2s9l7FuaQUlWHdNe6c2Chx5B4qTi1U/e/1vbaoeIf/f83R6gtONvoUGr54N5j6Mw1DP51Xfxd/Vl1Xsn8Q7ScMtjycgU/1kNwBcuFfFzSRVfJIYzwe/6NWlLpR6Zq/KaMudXYuTKkZQ2ilyTvxKgCIJA9TffYs7Lw++Zp5HfYK28YcsWyt54E7ebxxLwQos6bIHBxIs//ER0ZQkAzzzzDM7O1yYLG3RmSrPqCU30QnEFUbbKUMWolaMw280kesXz7eCXcXGJQya7sezXD0WVvJAp8iQWKFwZv7GEZ4Ny2NVZ9NO549wWkifdT77BxFeFYnrgaql9ENt0rZV65C4mJFLEAOFa52Kzo5ZJseobWfP+mxRcOMvgu+6jx7iJbKuqZ3+NlofD/P7SG/vfxaYvUsk9W4Xdks4tj/clPKETuoOHUHVMROHfIghnsQvYBAFVG146IE7IutoaNF7ejtemvgic3KhqPIWt3oQHfVBGujUvY6qu5dLQocjNJmo9Yjm96CZGuEyhrlxP+NAgnsguokir47f4YHyUbpzflgnvPYWbNh8A2eHjbH5tIbYyUZzv4e9XsnTBcdHRN1TDIKUEW7WRks5foQ1MQRDg6MYxSAQBJ6M/1a4yyoI9WfLEww7nc7BWy+Qz2bwYFcij4a2F8W4EhnPnyJtyOwAhX32JZsgQh2sjWK3Ur1tH+aK3UEZFEbZkiUNp8a9g5TsncPdVM3xWAm/fOQV1t2HMe3ru9VdsxzXRHqC0438GhSUVLHl2HoJUzpz3P8ZaL2H9x2eI6urLTfckttKF+CdhtQs8ll7A6vJaPk8IY/JV7aW62hqyT6QQ3b0XkmwLtatE5dKA53oh97i2KJRerye/Pp9zDecYHTkaN6XjPXkyv5ZKrZGRiQGtlGyNGZfIndCSfr76jRNEoaiGKgMRXXxarS8IAturG5BJJIzwdqPeYuWWrQfodvEEzn7+PHzrJBQyGX5uf62sVn48mwubj7Dcaxud4zJJUIhBxPBh2Vft34ZE0jrzkaU3cuupLKosVlIaXZEdLOE9w3HWeCWCAFNqdjPrjVd5P7+MbVUin6GtAMVit7Amcw0BLgH09e/LmTNn8PPzcyinALyWVcxXhZXM356Csa6BVKkRX0MVsY3ZzF26jrgD5zE3PcaKByexYP15juXW8Pn0bs1eQn8VtctXYDh1Ct8nHkcR6MijMOos5KRWEpbo1abtg/74ccwFhbjfMh6JQiE6I1utyBVXZRYzd8Km+RB7E4x9v5XhpE1rpuzd4wgWO6p4L3xmiQq9NpuJ3X/0w3y2IyQVcPPUvQ7rpaxZwcHffwZg0F1vc2xjBdV1e3GvyWNll7Ekz+rMpvxaktKOM3vIQEZ4eVP4+DzsUjnRy36m8Vg1DVvzELCR0bOc+zwiiSgvYcApM2fjQjkeK+qEbOkeR9cruukEQWD8qUycpFJWdf37fjbmwkLKFiyg8fARlBEROPfsiczDHWtFJY1Hj2ItL8d9wi34v/zK3w5O7HaBb5/YR6/xUThHCmx8cS6R0x7jtltH/u3jbse/f/5u56C0428jNMiPCc8uYNOiJo2Ujz9kxD2JbPvuPK5eKvpO/M95WsilEr5ICONgrZYcg6nV+KZP36Xo4nl2fvcFs+/5rPlzu84M1whQampqWLx4MWazmbFjx7YKTvKrG5my+DB2AfrHePPbbMcWRWVIMMqoKMw5Ofi/+GKr7esbzKx8+wQ2qx2vIBemvdLbYXxvjZaZ53IBmBfuz7NRgawbPZC6Ef1oqDbQ/529APxwizfDTLsh8Vbwv74Kpm1HFXGGcF4ufoDKgT9QU9O6c6KoeCkZGS8jlSoZMviiQ3AR46zi/ACRTGitN1FXY2KifSDr08sRkOB+6z10dnNmUWwIkdJCutO2kuiazDUsPCryfWb6zER3XCxHPTD7PoIOPgfpG2HyEnbrEwktK0ZSq2JJcjRCnglcQajYzsqUh0nQzCdVa2CQp4a00gZ+PSqSSUd+tJ+cRTdxMe0ZamtTSE5e0mLkBxQbzcxPL8RZJmVxx/DmjjBrbS1lC0RBuPp164g9fwyzuQpnZ5EJpNIomlvrjY06ck8dJ6xzMi4enlhKS8mfOQvsdqq+/pqY7dtY9/6bZJ842rrT6vCnUJcPx7+Dmz9odX0Emx2b1YpEkGCrb7mnpVIFblFB6PwO4eEh3jP61EosFXqEfgH8JHPDGtWRDjkXyEktQ0DCyohONESIy3aq34feqStHug3hyWCo/W0NlkzRKkG3cyfK5L7URGylMu531llnYJJ2ICMwghJ5PkaDAVDjYrTjc5X+mkQiYVawD4+kFVBgMBGm/nM12GtBGRpK6Pffo09JoX7jRgwXzmPX6pB5euI6aiTuEyag7tjx+hv6E9SV67Ga7fiGajiSKuodJSf/a9tsx78f7QFKO/4lJHWKo+qBpzix+C3ee/E1Xnr3DfpPiuHQyixcvVV0GvSvGXn9FUgkEpRSCbY2coJKVYs6pOuQUCROMpRBGgel1atRX1+P2SySBtPT0+nVy7EbSCaVIJNKsNsEXJ1a824kzmoCVnyGShGIzKm1OqVEApebZdqyDXC5ojTgrRR/qh4KOR4KOVtqa5rHog4/B7rTsO8deLX+mufTvN2ufmj3FaHu5E1C/JuUla3Fx8dRUbaqUhQMs9vNbW2iGXJ3J3zuTmQIsK9aj0QCoV7iW7Wn3Ypi7e+ctFgoDQrigQccCbne6hZeRoBLAFlkAaA2VYjBCcDKe1j0eBErTHo0hiws7nHIESfre/wP8XNqPdbu8wDYX6sjJkzNa/4H+LaiAy/PGENDw1nKyzcAcOrUHQwedKZ5n8vLathXK3ZiLS2t4Z5gUS1X5uaGKqkLxtSzeNx9J0dTRmMyleHrO5IunR2drrd++THZJ8QOkCeXb0Ti5IRUpcKu1+MU6IG9MpPc06LPUvqhfY4BSvdZkH8IIgaKjOwrgkCtVktu+jl2F3yMzWrhrjlNQbW5EcnuN+jp1Jdz8U+xvfgYqvyzuC8Tv/ev8stYHR0AI6fxrb+KbnJ/5u+dj0p2ATelN79Ef0tmgZ5j+ocJL7Vg+WMWNfZgrCpv1B4K5H3jOJY2Elu0AYXZTlVjIDQlI+90O8aYkhHUl9WRWWtn/bqj3PFyLweH8zE+7jjLpCwprmJBzN//7UskElz69MGlzz+jS1JVJH7vPqGuFKzNxChTExF24x1H7fjPoD1Aace/jOFD+lBVcT+FqxbzwVsf8/RL89FWG9m/LAONhxMRXXyuv5F/A07VN1JusqJpo+Y/7vFnKUq/QHB8IlKVHLehYdfdXkREBDfffDNms5k+bTwoQzyd2fTYQKp0JvpGtSZBpme8TEnJ70Dr8gmA2lXJ7c/3pKHa0KaOTC8PDdt7xCGTSOiocQxwRnYM4PUJHVHKpISXdoLTjk6ylfpKvjn7DV39ujI2aiyCIGA26HFydsF9TCTuYyKx1tYik7sSEeHIJQCIipoHEhn+fmNajV3GT8VVvJJVzKwgH16LDSbM25EPY7fbsTfJnJtMrbNaw8OGs+qWVbgp3fB39qcouggPDw8QBOw9ZmPP3Il88nf093Sl/8Bu1Ev0NG76hDzfEt5oyMJD0kht1Vykl0pxLUvlUmQCxhULmJhxhM5F3lQe+J6CMS/j4TmAutqDdOn8jeM19Hbj28JKaq02xvm6N38ukcmIWLYMwWTCImvk4sGfxGtaub3VOUiv0uGpU9s58+lD9DwhoMtfSvaie+gYOIBGVxh4n2Ob8zmP4dwvX4GyTMo2ix11E0/InHOYr5Ztx1JegcoqklQzDu/DLyISzv0BR79ECrxZ0pk0az0/X/yZ7W7fYW0w4WvOZvLJcnZ36EbgcSOuPZ1xstoYWzIWjV2F4kIuQWYZX9vnIdn9FovHLqExJIoXvF7g1SlxPKzPJqPKC5tdSg9zGW8UfMp7PjX0Kj6HruI+TtgEDKpS1IjaJ6V59Q4BiotcxpxQXz4vqOD+EF+C/kVOULGumJTSFAq1hUiQEOEeQZ/APvg531h33rVQVaBD4+WEykWBtjgPwSOw1XfZjv8+2jko7fi34YvPvsd4cA2aYVO5//4ZbPvmPAUXq7l1fjf8I/7Z71IQBEafvIQAbOwWi/J/4GFz/MRkGhrEwGH4oDSsRSeRW3Qg2OFKboezJ7iHQvlFhJzd0OtBJO6t3z4zMjIwmUx07tzZkc9ht0FFGoJPHPpGAWc3JYtSFvF7hhgcrbllDVm/b+TC3p14BoVw70eLqVu9htImAm7ciRN/q5bf72gaOQYTMrvAJfcg5L5qnMIdv+fS0lKqq6tJTEy8oQkgJSWFCxuPs6VTbw74KZgR4MX7CWFkNBpxzsxg3ua7yAgWUNllvJ1/Hwf8Y4g+upEKo0gANUmUHPXsSVjHUm5N2IREAvfyK5lDet/QPdHQ0MDBs1kcKrExpXckyeE+VFXtRqtLIzRkJnK5o/S92aAnL/UUIYmdcXZz5+51dzLoYmdG1/en3iqwVyeWtwJLD3PriqcdTPAWbrzI9wfFEt6Se3oytIMf2G0Y34rhPcud2GzgVFFE3wEDGXTnLKRSGZSehW+Hgt3Ky/3uZG3pAQBS7zhDZXE5X/3cEoTNNopZsaVRZ9CXVKMU5Iw1xrMr70cAlLZqVo9WUCOvJ6KmCyMv3UPu0K2sPjMIux3mlP5CR6dcMhrEYEDm1BWF81DqVeVkeZ/B16RivdCLi2+MdbgfdVYbnQ+d5+nIQB4O+3uBRFljGW8fe5vdBbuRSCQEOAdgx05ZYxkyiYybo27m6R5P46Hy+FvbX/fxaRROMsbO6cLCGXfg1KEnz7zcWgiuHX8N7RyUdvzPYu6j9/FWVQUNu1ewxs+PCfeOYO1Hp9n0RSqTnumBu+8/Z8J1skFPqtbA8qTof0twIggCJU8+ScPmLfg98wze9/51t+SE+EWUlq4kyBbFH3/8wsJUV4IDA9ny+BWaHIIAhlqozad45zRyJIGcOVRPg8KP5557rrmNuKioiGXLRMnv9PR0br/99pZtSGUQ0IndP14k/WgZKhcFXR7u0hyg+Ln4sefCWaQSGYmWnpx/ejUaa0rz6rbqKocApbi4mMOHD9OlSxc6dGhbWbOsrIxJjVUUHDrK6LAB1O4Q+4V9H+qCU0RLNiIwMJDAwD9PnV8WyZseP53Ao850Pryfl4aI5m+/ltXQPVvL02oDVqmc4EA5YMEotfFC937ka2QsPCRKnXfyvReJzIsTUiMbi7rgpmlgeNgBvhVmUGc8iZ+zh8N+tVvXUPnBIgydI3B+9D0iIyP46cefWVGkIaEqlXd3aljy2Uv4+AzDx2cYbUGpdiauzwAArBYLtRk5aOVN/lQSmoJRKVK7FbsgcPnO3FejJSbBm7jMSjzUSvpFN2XQJFJUXsHMLF9JoUsivT5fjuJKcm1gFy7N2cvq7PVMjhrHA32eZ2u9ktcLSngkUEFUVBQ5OTkMd+sORtD0D8JZ10iJtpw0SQDB3TSQJ24qJOQOfskS/XN25G7C5GrAudSABDsgRZApm4MTAIuLAruimk3xX9OgFsXx3j3lxJ6UJIb1adEC0shl9HbXcLRO97cClAvVF3hg+wOoZCoW9F3A6MjRzeJ79aZ6NmRv4KvUrzhaepQfRv1AuNuNmQtehiAIVBXq6Dw0hJKyKpwtOgJj/j6ptx3/HP77r5nt+H8KT738NI0BHchc/hWHj5/h5oe7oFTJ2fh5Kkad5fob+JvYV6PFQy5jgKfjG67NZqKoeCl1dSew2+yc3VNIRkoZVyYOzYWFVH37LebCwubP7FotDZu3AFDx7rvNn5+uOM3T+54mpbRlgr+M6oI8DNoGsNsh7xCa0nxiL5bg8sdjLM7ypAEX0kpbPH7sdoF1qSXsK7JBUDL5EW6kGxJwwoTFYsHWpA6rrTFSlqlrzkB4ebXdjluaI/IQjI0WxkeP58DUA5y+6zRuSjdGPvgY3XuNo9FWz7aCH1hVcgHn2ycT/OEHrdxjd+zYwYULF1i2bBkVH32M/aryjMlkYssrrzB67gM8tPQHYpf90DwmUcpotNr4urCCI3Ui6bW+vp6NGzdy9uxZABqqDfy+MIWvH9uLrtbEqsxVACxNX0pCdhma4ovMXfkHscVmnt1xgduPD6LowHCStOkMTXwUX4MLD5hm85x6Dt+an0In1OLk3oEDnYMoUkiYYlbiZIcQfREGOywqVTH8j4H8eP5Hh/MoX/gmpkId0s3n2fxODuf3FaGrM9JRe5EOjZl0rz/NuV3b2rzWbUEqlTLyUhRFeUfYV7mEc/JPOdDlECf9vuHN0VswScVy14EaLVNTs5lXUMrYiR14e6Q/b72xkFdffRWT2Yxw7zZ2BMxlh7ELv//+O9QVwCdJ8Ko7lF/k9ePv8FvG78zYMoMGqQ8vZ5XiUvgkqcf6k5C4ljm+txJZ4QGAx/hoXE5uJLToNCMLNzBmZCeUKjVyiYJkZ1HbxIiFzlk7GZb+DQddUnlz8G/8dn8fHvnwE3o99hxLJ9yPLqYLjf52dO6ZJJWMQG12pXf+LRSY+pD246VW1yJYpaDc/Nd/71WGKh7a8RAR7hGsnrCaSXGTmoMTAHcnd2YkzmD1LatxUbjw4I4H0Vv0f2kfjXUmsaU6RMPp02KrdWLnhL98rO3459EeoLTj3wqFXM5Ti17HoPHjwJdvk1tSzLhHkzA2Wtj81VmsltaS7P8KBEHgaJ2On0qqiFA7IbuqVbOg4FsyMl7m5KmpnD+6mwPLM9m55CKntxc0L1P81FNUfvAh2Te1tBjK3NzwffwxnGJjCf/1l+bP30p5i615W5m9fXbzZ/Wmenb88S4pC6ew+fHRZB38Dfw7iu2j6ZvAZmKB+UOGxfvx870tRNsNZ0t4/PczzPzhGL8czScu+VPi42z4+7kycuTI5jfntR+eImV5Ce7l3XnooYcYMWJEm9di+N0JdBwYxO0viiqcHiqPZvO88M7J9H7wLspsZc3LezzyCG5jx7baTnS02H0ltdmo/vpr8u5wFE+TSCREX8pEChhlCowFh/F9qAsBz/ZEGaTho/xyFmSVMPF0FhmNRg4cOMCJEydYvXo1lZWV5J2torq4EavZTuquAp7q8RSxnrF8MvQT4iZMxqSQM3HPWt5N+YER5rXIJGKZ5KOL2xn/myfjTo+ms/wQbtJqnBW5WGNd2Nq9F4fUUgw6GzU2eLxWSof1npi3y6m2iY+5L8584XAeikBxUqp3jQBg37JLdPAYSLRE7NCRICW0IZLKH85j07WQhQW7gE3bmjwslcmIePZN+ocOZlrIWr5gDBXVZg5qwqksfIWOr+zBZrOjXbmyeR2lVEpamth+bkfChYJi9uZoKagQg83s7GzIPYCtPo/CQBWVRxZQTiiCXbw3zn70Bp0uppNoP4fM6I7xSDz6mix0m+djSPmKPWe30105CHXkfHxHOzNh39fsSBqIROFMRccfKAnZznL5bnLGPITzgKf4OftNJnVbQv8YHzSeXgzsP4CHE6JQVpWgqqtFozHhrotiZNE0Ihu6A5AZ2JogLkCr3+KN4MMTHyKVSPl82Oe4O7lfczl/F3++GP4FNcYaFp9d/Jf2UdkkXugb5kpORgYWiYLO8f+5jsN23DjaSzzt+LdD4+LMAwvf4Ltn5/PHogU88O5H3PxwF9Z9dJqdS9IYNbvjv0UjpdZi5amMQjZV1hOtdmJOWGsVWYWyJdug8fAGagHwDmnJtCj8AzByttW6PnPm4DPHkdjYP7g/aTVpeKnE7QqCwGc/TiXgYi31DX6AhLcKNZwY5AGAod/jKPYuosPA8fww3FG+203d8mD31jjh4z+KgUN6Q6dMCL1i2aYHvdymJiCgtTEbQEFDAe6h7gyJiXf4vG5zLo1HS/C8LRZ1ki+W3nGYTtcTndwNd7+2xbQGDhxI96hoiiZPxgZ43nGHw7hSqSTuqSfZ+8VvPJc0AwDP7w+itQgcfXogAVd0NLnJpYSGhnLihNjJ4urqinNXN7JOVmDUWXDzVqO41IPloyahcJJhCrLx1sCpBMiquU+2GcuhQGoiXbCZJUhLqzkRvg27vYC000ZCA50pEYz8FnkWV0kfyrxjqdZI8dbZSY8+wJh5zxAW2YnKjN85U5rDggHzHc5DP+9lns8vxqQz8IDBlVH3JCFXSinLjsE/8mGEkkaqvjsPQMVXqQQ+LX4n1b+lYbxQjdxXTcCTPcTgRQCZq5KXKxop6z2e1019eFTqycLdAYQ6qSlE9OuxVlYS+eG7fBYVi9RuZ9LWjRQVuFPg5s7ipAH8uDYbabUZ6Mrn/W1g1LNoczZ+bjOI8t+K+6pUTP5j0MkHEG3Kp6RkM2NIhVgJDYp6dgWvRlrmxV1SJ9y0x3FfFcR6+2i+wwgZszD19iW3q5KMqBDU9kUk1ERhkStRWlumgsZ6LR5+LYTn+7p25tStk1ibtYtNrpfo5zGMH2PjCTBV4qIzE+LUOqNnsNlR/sUApdpQzZa8LczrNg9P1fVFDUNdQ7kj/g5WXlrJ3OS5OLWhFtwWqgq1OLnI0Xg6UVuYi83VH5n8rzt+t+OfR3uA0o5/BMGBfkx67jXWvfEci196kac//oib7uvIlq/PcWi1EwMmx/5L26+3WLn9TDZFRjOLE8OZ4OfRSggMIDhoGq6aRFSqQJyc/LnrDQMyhRQX95aHWdD772G6eA9OidfXEHm82+M80OUB1HKRT6M7tomgM/eit7vja1hO7kBnFo4c2rz8i763sXTAEACyrTZcrngQDu3gx4ZHBuDsJCPatylgkkgBR976xPndKMupJ7xz247RO/N3Mm+v2Gq7cvxKOniJvBG72YZuv2h2V/N7BiHJfsy89z5stlnI5df+6eemVpJ/QUvyik24esiRubZuxQ4YN45KdQdo4p7UWgAk/Hzfc8xb8yW93F0IlMlpNNvZrPQmo+9oBkUHoVKpqJNZufXJblzKyWXXe+L6qbtzmP5GFPf+eJ4Ut64AHLZ0odeISsJLOzL+1G7SAyrAZkPnbOWii4W06iT22E4TqrCD5Si90nSE9dnGJ/p6bHYrBevWs+qRI+w6Gs+xPD+27Dvs4Dq9NdiNo2Y94El9XAjObmLHSVhTR5U9SILc3xlruR6P8S1v2OYCsUxnrTRgqdBT/ulpsNrxvC2Wm33d+b64iviAKO5Ljua+kZBdqeP7g7mM6hiA0s8bzYjhhB7LIb3vPNa8fZS43+YS5uNBedehROpqCDK5EuN8nP05u9jtVo2zjzfDSofhfiQSzzOVlIwRj6/ew4WOPbJR2S0Ur+hIeF4WpaMV+GnyWHVLbzIDt5LmvI3p2YFgFgNXN5WUesBbfxR1zjQidL0YKi0jXVrCPvlFaqU6/CpLuT32Co4T0G3MeFRVcWh+XM63iSo+XvYGI7RH8erQSOPE1urKnV2d2VBZQr7BRPgN6qGcrjiN1W5lVMQoh88FQaBh82akamdchw11GBsVPool55eQXpNOkm/SDe2nskCLb6grEokEoaoYp9B2B+P/VbQHKO34x9ApMZrKOc9w7Is3+eClV3npvUUMvD2OA8sv4eqlImnYtT1UrocXM4vJN5pY1zWWBM21ybcSiQR39+Tm/7v5tF5WqlSiThaXMen1bPzkHcqyLjFt4ft4BbXuplELEsg/DIY6TApfDEIjEgmoFJ14ZUQwEl+P5mW9r7Cal7YRQHUOuSqNLZGIxMoroPF0Iqb7tcmGBdqWclWRrqg5QJEqZWj6B6E7XILHbTEs3pdNRpmWF29OwEfT9k/fbrOz7dsL2Kx2LuwvbuXhcyVm9AmntMGIh0xg35ZDKE0GJib4IJFICG0U2PfRR2hHbCas0gnTpX6UH4GFE6fzRY0eiU1LdP4LTFA8ibPFjaqwNDJ/OMwHxUNZhZmPMFIaE8mqeDFYyY2OIPGEyAdx0yuIMN+Jz8EQxox9HqWmCkhBm9+b4HOzCU5cxpRlFnqlF3Hpp17I7noDaK34OtTLjY/yyjELAqN8Wncc5GLDeH9iqxZvrzviMZyrQtM3EFudCazi92XMrOXNOxN4PTbYobwR7ath0cQWX5nQzz/n0s9pNB4upTFPT6gmEKU5H03NT9xu6oLS4EsCRobVpkItDAgTyHPyJs46hapb3Hj4wlZ+SRrM2C4HUbibsQHbp/dkm+Q19syZxvI7fChwLSDNWSxt5fid5MGiLqRJjvF+t8c5++Ep4hjHngYrBW71hArelJcfweBTTqitnk8ORnB7P8cABSDRpwN+t96H61df0eVAGhW4YzXIeMjtBGuSHdv2ZwX78FFeGavKa5kf0XbW72oU64pxljvj7+KY2dPu3EnJk08B4DtvHj4PtujpRLqL4nlF2qIbDlCqinREd/WlvkGHi7EGr6ioG1qvHf95tAco7fhHMXRgT6oqHiR/xRe8/+YHPLvgGbTVBg7+kYmrp4qorn/d3C9NZ2BleS0fdgi9ZnAiCAKCYEYqFd/eXs0q5oeiKj6ID2VKQNskU4DiM4exX9qJWe/O1q8+YvrCFvOwlPU/UJF5kp79BhHQbSw4ueIDjJpZQMPenSTOTkbS1XFCfy4qkOHebnRwUaG+hieLAyRSRxvlG8D0+OkAhLuGMzzMUXDNY3w0HuOjya7U8fYHojPvmtPFDpkEx91L8I90oySzjtCEP0+ze7oomyfeJweHY6uraybcVhZqSQj5mSKZkVxD1+Z1CurrACUSwYJWWseyrm8gJYhxvg34HBbVdieh5IDfIeo0/TjftN7oUUbO1vmhKfJC5tQZH73YNVJ9cRyBvX4EwNXzIPnOSvoe7EZ80e7mfd6XdZCE5O4Mi3XMQCW7OVMwpO1JLbPRyNDj6VgFeCTMj/6yDL4+9S23hU7k9p5TUEV7ACD3c8bj1mgEm4CmbxBWsw2T3orLn9gnACT2CyT/QjWVagnfjHuOhR4K5rukkn+2ijDCiXDKb15WUzqeRu8ABtWLE321p4oZ6b/SUVeCYrDA0tJb2S/tA1EC1bMt+OXl0quhBwn2ELZ67uPhsjvwRUVwwCqKVw4k2A7HGq2c9ElnUcf9KEzpPHuyIzOdRHG8kVo9tasy8ZzUOsvpExNMt1B3BMQmpS98J3DC5sah01vo37VFM8dZJqW/p6aZKH0jkEqk2AU7giA4ZENlbi1BvNzfMVC3CiI/SdaGJUNbMDZa0FYb8Ql15eSZi0iA+I7tBNn/VbQHKO34xzFl0hi+Ki9Hv28lX33px0MPzURXa2L7Dxe4dV5XAqKuTYZrCyvKavBRyK8ZaAiCnVOnZ1BXl0JI8F2ExrzC4ibTukfTCprXKywspLi4mK5du+LUpE8RcfZ1osLFabF6dhP5rrEKY+5Rzp06ShFBnNyWxqt9pwKgO1aKT7WVqMfvQuqsuOo4BI6npFBXV4d6yBBQyFuNnzhxAq1Wy4ABA1AqlUDrDMr1oJKruLfTvX+6TLCHmg7+rmSUa3lx7LUfyBKJhAnzumLSW1Br/lxky2K2cXxDLiqNgq4jw5C5t3yP0cl+lOxPwL3mBKF+GTjLb6drry4ow8Lp+NsH9Di1g61u97C/g4DZqz+3H28AQYWcfNTyXyituI/KCgPPVK+lQGcmn3O4MQK5a2cEINvFTheDlIKyPE5mTKDfuSI67T5HgERCfpd66rpIKC+bSJ1HLFqncFwOf0jKYfD39SC2Z1+H8xAEgdraWjw8PJo7pXQ2O1YBvOtqcDt1kAXuy6lV1HHh4jkyfy/gxQ+ebL5emj5BmEwmvvz8K6wZochtLvhEK7n1sZ7N99XVCIzxwPmJBD5KFzvHlF6eDJWXsTjydQK9jzC29C4arM6Y7fE8OeArMqrO84L0QSbbXWmUiF1VGcX+BKwPYqvPcMBEXMkuvBsmEmcLIF6ZTJdaGJCqRHXqWbRx3liUApgluMqgj5uC8sp4IveHsLy/hAvqQtKMNSSoDlNX1RfLwbYDFIDeTzxN3U2D+HnT93wXMQO7jxORW6ZA1yyH5YKclBz6CwFKhFsERpuRAm2BQ+uwS+9eRK5dg0SpxOmqbMelGrFEeDmTcj1UFTURZENd2bs+HRtSkrvEX2etdvy30B6gtOM/gjkPz+LtinIa961kpb8/E2eNZP0nZ9j0xVkmPdMdD/9ru/JejVStgT4eLiiuItrmnatix/IMrMnORHmdQg4UFf9Chw6v8mCob3MGBcBsNvPTTz9htVrZunUrr776KgBSU4tUvLc5Fy6lY5PYUMYNw72jgaILFxjb1PlirTZQt1p8KOsOFhPy9kCH46moqGDr1q0AnDhxgpdeeslhvKysjE2bNgFw5swZ5s+fDxIpdqOFqq/PYs6rx/+Jbij8W9osd+3axYEDBxg+fDgDBzru78+gUsjY8vhALHY7TtchBEoloDbmgyoM5NfOBGQcLeP0DrG8ZDHZ6H1Ly+QhU0gJeGQ5aT+m0lBr49aHk0SOx7Fvebz4PQ6q78GGM6EVF5hSpMLN3AUAe302K41yJul/pJJAKDqDJiaS24zPgAR2uRaxrKMrZ8JDWAo89VU5zi6hXPBLpHKoN0P37AVnG7IaPQqrHrnNiM2+tvm4/MKjYPPTcOwbzJ1noR/4FodP7OXYsWMAzfdBVzdnfukciddDi9AWZHNiVCC1MXX4ayMo1jTyy4UlTI4ajlotljZKi0upqqrB2yaW16qyzbz11lsseOUVapcuQyKT4nHHHQ6ZgQGerkSqleQazDwS7ocnA+jgHcclMllfImWSdSZVIbtAImFZ1kSK7TL2o+fuEE+UOgU2Zw3jpKuQWyxYBQXdceJHuRsjrmjL9crcgx3gUjWKrpU4Sc9gsidzwNBAcL8luIacZuCvMlKUi9klBFBcHIlv/gaU1nXY3xqGVNW2EaVHx948EhnHHUsm4pZ2AVVw11bL6Gy2NlWdr4Vu/t1wljuzPns9j3Z91GFMFd92ELEhZwP+zv5Ee9xYF05VoRaZQoqHv5qq/Fwszt44q/+a2WY7/nNoD1Da8R/D0y89xRtPVZPzx2IO+Pkwdk4yq949yYbPU5n8THfUrjcmi91osxHt3HriPHdkM78NVpEujwN+Z4fvD8REi7Xr12KCee0KbxCpVIqzszMNDQ34+vpC4TEw1sOA+VB2Hno/CB6h5BX/SHb2u1AIU6ZkM2XKlJZtaJTIvFTYaoy4DmnNp3F3d29WVRw8eHCrcTc3N1xcXGhsbKRnz6auHYkES6kWc64YRFR8mYr39HhUHcSsz+HDhwExULk6QBEEgfz8xTQ0pBIX9woqVZDDuFQqwUl6A6nwvW+Jvj4Ar9SIQnBtwC+8hTwb0bm1nUHBhWoyT9cBsP2789w6vxs6k5F9XgPRGgW2xX2DRW7hzvzk5nXqD6zmptG17LNF0ffcSTwMJjhxAW5tujyKYkrcW2wHbkorYHVCDwAq/P35ZvLdZHeIZ5XawsQt27gUrmVuwiyCGhV4jItE6aOB07+hl0j4qGQdh5aWMsjm2F0FYDeZSFz0GtrMS9Sq5SRdguS8UVwK03EgdgWjKl04XP4GGk0CPTuvRbasDBeDP3qXAtSNwdT4HQdAt3s35W+8AYAxI4PAV1+lKKOWXT9dxC/MjUMPdOLC3h1sm7OQLsNG8cesP/j1+XmU537LcqDbeAUv73qBMr3Iy/BHwnhLb3ZojrDTHEV3RRgPCxdBV4uQm4azj4wjnnNoVNkptwpkhPegf2kqcSOiqTR3Qq88wB86Ca4SP/xCRJXj+hk23L49jHvVPg737oW9x20MMXfE9dXjBL85AIms7W4cqbMnfnP3glkPytYvGCfr9fTxuHGFYheFC1PipvDzhZ8ZHzWeCPeIP10+tTKVtVlrmd99fnM7/fVQVajDO1iDVCbFXFGI3Dfk+iu147+GfylAefvtt3n++ed5/PHH+fjjjwF48MEH2blzJyUlJWg0Gvr168c777xD/DUi4Hb834FMLuPJN1/jvSfmcfird/F96W3GP5rEyndPsunLs0yY1xWF8voTqEoqxWhvXQZRB22iQTaj+f+d7AORqNv23JHL5Tz00EPU1NQQrKgHq0nULbkKtbVHrnkcUicZAfO7I5htrco7ACqVikcffRSr1YqqjTdRFxcXHn/8cSwWCy4uTQ9yiRRFoAuqDp4YM2oRTDaqllxozs4MHTqUw4cPc9NNrY/VaCwkO0fkzFRW7Wjl/2MyVWK3m1CrxYey1S7w9KVCjtbp+KFTZAufpzKjZaU/4cP4hbtx/8eDkMokyBWtv7fAGA+8gzVUF+voN0lU6nzMfSybuwxEYasmTpf6/7F33uFRVG0fvme2b7Kb3hukEnpHehWQIooFsfeGvXfsYm/YXkUROwoKKIj03msIIQmQkN7b7mb7zPfHhISQoPi+FvTb+7r22t2ZMzNnTiY7v3nOU6gIrCZLW0T3xlRkWULEQZxfPW5ZxKrXEmh3IqgkKlSvYqlxU1kjcL4+jyND4hjCOoQIJ/FZJRQn9ieoPp6xZ3dkWnAjblnm1ctT2dU5Cfn1vbiAikN7lHEc/zyLt83maz8nkMV872HuTrybUf1b/Ifsu3c3J+qL9/cn8NLpNIr7CY9J4bzAc9EXPogXsFqzqC7aRGXM+4xjDA5LCqmzulNSGkdycjLew4dBpQKvF2OTCD2wtghrjRNrTSUuu4edP36Px+lk97LFjLz6RuqHR/BT3CYuMI3DHHwN0/LuwevwUhx1KfdqY7GE7qJ70mayi+C2ojuZLOxll64T/TqKvKl/nzU1oewvKkMX1Miw0P70SX6EmzvCdVtWUuiBsIRMGvLzqdgTTGCChf5HKylPy2ZPdCdklQaTpCdfrCBZikT2SAiq3/ifbEecHLM7OWJ3Mik88Ne3PYlbe97KuqJ13LTiJt4b8x6Jge07sGZVZ3HNz9fQNbQrl6Vfdtr7ryy0EJUUgMPpwmirxNRr8O/qn4+/lv86UduOHTv44IMP6N69e6vlffr04ZNPPiErK4vly5cjyzJjx45tzorp4/83/n4Gbn72OTwaPQtmzcTqsTFpRneqi62smJOJJP22g6hOFHC0U7JYrhnJPdKLXGxdxOLVxTxdJvPee+/x5JNPKgmvTkSSMDorCaneyayfMrlsmYt6e9vMl8nJDxEVOZUe3T9qty+CWmxXnBxHrVa3K06Oo9VqW8SJskdEjUDoNV3xH6RYQAw9WhyJhwwZwgMPPECvXm1N6jpdBGZT96Z+P9xqnd1exJato9i8ZTj5+e8CkGG181VpDXl2F2N3npANdNzzMPRe3Ff/xOt73+bN3W/ilprGpuIQfH4hbHwdZBmtXo1ao8JZY2PX/O/Yt+FTLJY6AIxmLZc83p8Z748ivKlGj1MWMNmsjC0/SlXxnVgPP0DNUSMuzw0EaS7ihZtv4bAlhgvjDxDZv44VVw5i+Q16emvWMDxiIymmKgbYtnINH5JCDuuDezErYSLbbcEEln6CZ0Mxo0zKeF4QEUR0qB/aOMXSYz67ya+hz9X0nPY5YtPPn1vlZvDIwQTV6SjZdgSHw4G+W/dmQRE7+236TrmMYZNf5PLe9zI+cQLdu31AQvyNDOy7EuP3F9M7fykB4Y/Qe+YAAgLNpKeno9Fo0Kenk7xmNSkb1hMwUXFM7jw0Gp2fmpi0ILI9LvwnX4zWYKTPpPMB+LR6IZV+dl4x63l0UwWl0bcgEkiX6jnUxa6mvPOnGMxFXNl5PtfkLuGgKY4STSR6lfIbG61eQ+wQHYJwKw0J/nxTP48hGRvJq9uNx9pAdWYhXS7/npLtERz8Npkgm4eeHMQr2pluP4tprsGM8XTnOYOb/xwsPuW1+2vE6bWMCzUzt7iKGrfntLczaox8OPZDdGodUxdP5dmtz7ZceyfwyYFPcEtuUgJT8Mqnd2/xuLzUljUSGmdif2YOatlLcnrqaffNx1/Pf1Us0Gq10rt3b959912effZZevbs2WxBOZn9+/fTo0cPDh8+3Jyh8tfwFQv8/8HB7Dy+f/oBXMZg7n/jdaqONrL03f10HR7L0Gkp7eY0Oc49hwrYZ2lkVb/WVjlrpZ3Nb+4h1O5mdm8vjhA1qVuVaI7g4GDuuOMOpWF9kTKlE96ZhYVG7vlWyeMwsXsU71za+8854dNFkiBvLSSdOrz3VLjd9TRYMgkM6IPqpKRV9fV72LnrQgACAwfQp/eXOLwSV2YcJbOsgXnaIHr2jUFlbplmW5L1E49sfwiAUVGjeXPsG7DwJtiv1PjhrgMQGIen3knBC1txGUpYIuVhE5Tqz+PHj2/Tx1q3h7zzzmeT3p9XUxRrVy9XNl3N9ezq0Jn4mnKqI/34NkdJqnZd6u0gu5mTqzgszy4cwTkhVyOH2tg7NJ37FrVkxh1bsZKHQiYQfccAiDaiV6mbhlRC9rpRaVqPicPjIKc2h2j/aMQjMh98vo8wwcPWyC9I7t+F+wbcBzVubDvLMHQLQxvTuowCwJI5LzK58Pnm7/P7T+fic95rTq4H4K5oxHWsAUP3MERdizXigKWRs3cqBS5vjw/n0SRFkD7747PkZOewp8NUblph5vieOqhfQzM5m3pLJEVFnYkKPExYfD6ZFVHMsZjw2KO40xtCd4vEQXci9krFGfrt4FqeGfwcKzaeTdrRTKIGlBOR3IB5GwSG2KiuS2NvbgzZKW6miJNIdihWx0dDRRIqPcROS+TG3qdOCSBJHsR2plgqXW4GbM3iptgwHkz89XpMJ2N1WRn4leLM/OP5P7apt2N1Wfk86/Pmit2zR89uzk10KiqONfDtCzu54ME+LF+/gqqlc7n6/S8ICfp9Tvo+Ts0fff/+rywoM2bMYOLEiadMuX0cm83GJ598QseOHYmLa/8CdzqdNDQ0tHr5+PfTOa0jQ299CKOlnFcfmUlMeiDDpqeRsbaIvSsLf3XbswL9OWh1cMzeukaMf5iBsc8OQnNHCDekWZnX0UH/nl3o0KED11x5ORxaCjnLwVIOXadCeCfOSgqjQ4hiop4xom3BsBKHi70N7df6kGWZl5cf4oo52yiqVdqUVeSx6a6ryH/mCeTPL4angqBgG7IkUb94MZbVa5q3l2SJ13e9zh2r76DKXqUsbCcPCo562PAqHF37q+Oya/cl7N17BWvXtSSckyQ3Xq8Dk6kbndNfITXlcXr1nAeAXiUyv2cya/JVRPxSROnzresLmaoiSC0I4qwDwXTM6aIs7NSSGt/jVlPx6quU/bQILSKyIwSboKSA37p1K+9efylvXXURzkYbsizjLikhUK3C7HSQPmIfQ2M2c1bUDs6P/5bAmkb652cR2VBDl5xC1lguodydyD1LvmLU/CNkLouhu+Md/MPuRCeGoq9J4PDPP8MJmiPaUcr27hau3Hs9/T7vxbc53yJJTnbuOp+1GzqTf+yDVuenV+vpHtadUEMoX+aU8wFOZpm3sy1wL1/kfMHTW56mdmEulrVFVLy9p/WfJKeWhn1HsWTvoWKfCWe9miqVSJ893/LTWy9TX6EIJ1mSqfxgH7ULcimZubnpbyIza9khnlma1ZySr97TYgVQZahItCRyQcZe8sIcAHTUyvTwf5CMPVPYl9GX+rooDuUr034Z6mokbRFiwA5eSe/F9rRCDkaJSKKXfI0bh1fPsvyzWXr2hYzPPky5qxc/ZU3EHOllr15F+R4jpf47KVXv544OL3Cg+6s8LzXQ/7CTiHov7v/knvKa27L6Qb5660F2bny6zbowrYYp4YEsqaw75fan4sOMD1GLamaPmt1uMUB/rT8397iZD8d+SEZVBs9ve76dvbSmssCCIEBIjD+lRw9j0wX6xMkZzu8WKF9//TW7d+/mhRdeOGWbd999F39/f/z9/Vm2bBkrVqxoCqFsywsvvNDsTBgQEHBKIePj38ewwX1InnYLxrJDvPrMK3QdFkPv8QlsXnCY3J3lp9xuUlggQRoVs46Wtl7hccH++XQzqBnYexLq5FFM6B7G1YNjMVXshOTRkDoOYvs0bxIdaGDt/SPJnzWRztGtFf++feUM3ZLF+F05XJVxtE0/csqtvLPmCBtyqxjyoiI8vnj1SQ7WT2F1bmdK5+9UxMbnF2BZvpySBx6k6NZbqZ6jFNfLrMrk4wMfs6ZwDRMXTsQlSczdVciWPWV43SeIlHUvwaqnYd4UGop3nnJc3O66Vt+PHJ7N2jVpHJqfxO5FiQQFDSAu7uo2T7uWE/x5CmtaxFiPlI4MOmCmU4EJ74ZFysLOU2BmHTxZT/VHn1L94UdYnp7JMvbiUrsZLHUgLiqXIWkG7JYG3A47+fv2UPrwIxweNZpDnbsQ//EcwuK6cXWXr7mh22cEhFlYlxBLtKTkKckOzubZzus5pitDLBXpnZ2JWC+zSvswF2hfwxZ8kOwx19Fz0BdM1mfQU3WMS4q+plQfyc8IZNUotW2e3vI0TmcFFosSNn7kiFL00baznKKHNlD81BZkSeat3W/xgWU6hoT3kOxx6EVlSm5iwjDU7USXuQotVH18gMrF2+m0K4PqLBNHl4Vj9kqEN4oc2ryOj25vqdUkHPfPafq13VNYx/vrjrBtTxlBGbV80DkBm8fLjZn5NHol4uMVC0ajG35wOFgV4KK7Ufn9HH9AQ89DSuSUzaQm29qNzvkTUAkq9KKJmw4twmSp56uUvrw9Qcv00EfJN1xKz3oH3XJrcfS6g6GpTs4N3Yy6UcdFFXV4IgoZekhi5D6ZVz704g7PIDBmCQmm79EKNr4b5I+nurrNOEiSm9ULAgnfpiPyx5EUPbwB2dtaXA8NMnG40Umps23tolNR2FDI3My53NLjFobHtXUuP5E+EX14oN8D/HD4Bw5UHfjVtlWFVgIj/dBoVdjLCpCDo3+1vY+/n9/lJFtYWMidd97JihUrfnVe/bLLLuPss8+mtLSUV155hYsvvphNmza1u83DDz+shFc20dDQ4BMp/4+44PxxfFBejnXNfN6Z/TG3zrgGa42DlXMP4hegIzolsM02RpXI08kx3JZVQGf/cm5POJ55UgZ9AIQfn/oRaTB3Qa3VYjS3/6Qke2Vkr4R4knNuVZGVpXMP0jgpEESBAxZ7m20TQoz0iA1gX1E9L12g+H649X1w6ZQ+O8xxQDVcvQTVMUfzdppoxdzdMaAjyYHJHK47zOMDH+fDw+W4PzlMpaqc/2zcwC1vNv04B3Vo3vbc5Vfy0fmLSQ5qbe2RZJnevT6jpnYLkRHnAnB0ywfUHNEwK8KPCNHJy6ueIXLie23OY0WKH2sLKtiHh4cOVXDVIOV4AWGBhHdIoiL/CN1GtRRSPD59oTvB8X3K9ERsJRUkp4xldOKl1JdXUnGwEoO/iaS+Ayh4vqkitCyjjY8nPfpLHp/7GA3+HraLgynu3YGXVlmIObsDtt4aqkvzOPz6F/w8LJj+Xg9DGxfgJzZA70S8KXlQ6kUjerlyTBrdepzLmz8PILQ6hzvG9+Dl3GvZVXqAl0Y8jcEQQ1LiA9RZ9tMp9XEA7AcUa5VsV3wjVuQrGWrVxmNsFR4hI6UHjbZcrDk3ETj5MGJvJ4dKH6Js11v06jkPQSuCCDpbDMaoZORixX+n5pqdrP34IyCTHmMn4nRWUln5C0HXD0FVY0bXlNwtJcKfpDA/jlTamDUsFZsksaCiDgA/lcgjQwdT8tO3mLwebjblccnzD+EsXoFJM5ItH9iIzrXRa8s2/nPNBQz3P4yhdwaX6S+k/KtfWNb9fA4kdWLCqu+IDLbTW1asH5c3rkBVMIWGgM5UH+nJbX2NkAgd9h9inedWLkmK4nC0oqBexcNDDQsw+7nAbx69VyaS+7yDtKxMRKHlmbawwU3i/s1oe5zDA/0aGVjl4eLqRiLCW6bCxoSY0YsCSyrquDHu1NmQT+T7w9/jr/Hnys5Xnlb785PP56OMj/gu5zu6hnY9ZbvKQgthcf5IkoS+oQxjaltHcx9nFr9LoOzatYuKigp6926Zp/d6vaxfv57Zs2fjdDpRqVTN1pCUlBTOOussgoKC+P7775k+fXqbfep0ulMmM/Lx/4Obbr6SlyorsG9YyPzwcC68cgK2eidL31NypARFtg1VvCAiiDy7k+eOllLucvNIYjRGta7V9EhR1gG+eVLxobjg4afo0LNPq33Ul5RR+2EWarsa87gOmEe2CGOtXkWQW+DydRYMfUJ5bHLbKDS9RsWi24a0ynw5/epprJhzgCBqSUgJg/MOQkAMftHQcdEiRL2uOduqv9afhecuREZGFES+OlZBcVNIpyFIeWL2yjJy3+v5wV3OKzlfgVPD8ieeYVFFNde8/j7B0bE8mF3IpyXVjAs182m3K5r7Z1ltoqgxmHFZ8MPQEuoPmYlsJ4HsRQMTOFRto4dZzxVntZjTRZWKy55/DZfDjt6vrf9F4Pnn4TfwLFRBQYg6HZyQlDUoKqZVFt6oF16gftEiAi+YCsCRShvfHB6CrBLwizDw3WEb/h4wdAnD5KcnMjmSH6JXI7ndbBW1DH92Gx8WlNE5NIp1WY8Q4RQpdQsUl8SybMHPXB50kIcaXuTYC2bWhs6muDqOs7fv5sisKJ7O3saOsh1c5/qau/rchXl0PLLbiz49BAS4v+/9zFt5F5dVlRHQaSR+Oi3ORuXGLogCVa5lWK0HAaisXEFwwBhUVyczu6SYIONYRpS6SDjrXkLCEpj25Cw8bhcarY49e66kpnYTQKuIKvP2N1hleRZ5wHSEnhPJtzsJ06qpdHm4PjaMb/d/gFflQfTCgJEjKDp6B42NR4E3cRmuomP+CgBeefJVSt5VLBOl5Ys5EpfAgaZikUtHX8ij7z1CTmwsgdpaltbeAgKEqUXy1S3+MTatPyqVjNHsAZRr7stVt2M2vUE/bzX76YwrUUeQXwozfryDdye93XytZ9cXsCzxLNb7JyDv9bJ6ZDQBh97hyvAHW85VraKbv5G97Qj8U7G7YjcDoweiV7d+oD1otfNZSTUXRATRN6DlN0ElqhgaM5StpVtPuU9JkqkutpLUK5zswwVoJRcJKf9bPTAffz6/S6CMHj2ajIzWhaGuueYaOnXqxIMPPoiqnXA0JeW4jNPpbLPOh4/j3Pvw3Tz7QDX5Cz9kXUQY59zUm4Wv7GbJ2/u44IE+rYr7gZLB8/6OUQRp1Mw8XIxOFHk8KRpOmL6or2iZJirPO9JGoKye/R79JMWPqmF5fiuBYg41MH3mANxOD6GxbYvlndyX4yR0jOD6Z49bdC5s1U6f1jZiQBAEhCY3yEviw9h2b0/Cc2roOPosShwuJuzKpczlZm6Xm7klMA7/QxbyK5Tkb9u+/45zZtzVPMe/vKq1/5ap23jYpiQg61ETStg1rZNfHSfEX8cbl7SNCgJFpLQnTo6jOUV15ZMxdO2CoWuX5u+pESYu7BPL2uxKPjyvJz0iTAhqEUEl8kVJNcuq6jn/rIEc2rKZ9IQOzHr1LYr8A3i813DSy0uocuqRZRF7jpIh+PPazkQV9cLh1jG++F0m7DtCSBcL1m0vsqNsBzICcw7M4a4+d6GNMxF2Q0v04bD4EQy7agc01oApgu5eOxZLJmaz0iY8YhJlZYuQZS+H11WzbeHFgMjgS2rRSSVsVXWhPvMmQBEiGq1yrarUJppzwp/Irk8BEPZ9Bee/TweDjozBypN/naOO93LnoB0movEK3HvZNezavYLGxqPo65KYLAzHHrITb3Uu344bi4ZKnOgpirwISXiP87fvIifEn4c+fZ+K0CiWFItAR/w1u5gQ3Z2fSpfgKMzi9oNxVIaNJcth4F7xGR4qf4v8Kgsj5Tr6xa4nRxuJxhvHyDKlOJ8h3EHcpmi6V3dn3SXrsLltBEgH2dC5D9R6EdwyyBDq/gR4sNXphmhVNHhOP4qzorGC7mHd2yx/KKeI7fU2Pimuomxkz1brYk2xVB6tPOU+6ysa8bgkQuP92bJPSR3Qs2eXU7b3cWbwuwSKyWSia9fWJjQ/Pz9CQkLo2rUrR48e5ZtvvmHs2LGEhYVRVFTErFmzMBgMzdk3ffhoD5Vaxf3PzeTFu+5hywcvExLyPJNu68GCF3fy0zv7Oe+eXmj1bS/XiyODeTy3mKR2KqZ2Gjwch9WCzs+fLsNHt1kvBKrZk7sKkyaYoS/f0mZ9QNivRwX8UchuL6hFBEHgrIQgcAeAKJBhtVPmUkIs3y+q5YfeV2FPsPDzkRp+qTPzdmEKSa+uZdYVPfmipJo7Elqb0Efe/jBBaT/hsjdy3ZCRBEb+vkiKPxOVKPDKRW3r4Di8EvdnFyIBZUcrePvzLwCImiZgtAqkbMzk3eRbubvrASYlTmJXro6fMkq5d0gkK57QI55QCfpwaQIh33/D2LMf5AtVV8wqoU2dl5YOacCkCEuVykBgYN/mVSb/TgwevAGArx6/v2mphC6gBICulkw0OQKOnq0DIjs6HiJ4xSUIOhF5hIxwPPPxqMdg/cswqK1gDNAFcHbC2aw4toI7+yrH6tljDhbrQcSiWBq252AYch+H6rZQJ+3l5+JCbIGXcHdoItcM+pCyxmzsL9yH2utgTd8J1NQVEuaqYmH4OIa463C6shAArb2BMHcuwdoGAur6s7i6O/pGB1tCwpA62bE4LRjdRrx4UKFGsHlw+ddwi20gCxet4tO6l3HZg4jSXw4OB5EhlRRvKiV6yog259SeRvs1NKIGh8fRZnkvk5Ht9bZ2t7F77GjEU4f7VxZaAAiLNVH4fS52tZEO8WfO/4OP9vlDM8nq9Xo2bNjAG2+8QW1tLREREQwbNozNmzcTHn56848+/v9iNOi59ZlneP/+u1n04lNc8+LrTLytB9+/sptf5mQy4eZuiCelzs6wNCIDvQOOOzMqP4Ver5ODWfdiM28nrddn7d6UJt31IGVHcohMSgWVSH5+PlFRUc1TjrIkUfr441h+Xk7s22/hN2hQq+2djTYWvDCT0pxDXPLUS8R06tz6ALIMyx+B3BUw9QOIaW3BAcUfovpzxamz4fYg7jy4nHSbiktCBjAy2MRNsWFYvV6eTVGSqxn8TZz/wBN8+s4mKKzjSKWNc8MDObedhFhFxcX8uGMvAJ3H/L4HhGqXh6/Lahga5E93U/tlCLwWFzXfZCM5vYRd1xXxJAFptxeRefBeRFFDj+4foVIpJnuv1UbBlVfiOHiQmLfexDhqFMuXL6ehoYEpU6YwOsTMiuoGLqTFsTL6oI2ELnX04Bv42c7d0z8DIKU/XNQnBpulAZshkK1+PelTlMeS5Eg8XZMJqOvPbSuf44txr9LgbV+cbKy18F5BJVfFhDA2tMVXqSi7FmuNg9T+Ec3X3cirbmDbou85fLiBkhIver2VmA+d+Ps1ElHcC3m4tzmxmfOgYtGSnRJIslJDAKDHJcqrHQRB4LURr7Ezv4bNR6qpTnAS4m8gW07jk9wjCN46+tZtp65hDxlnlWGUHWhr3keYv4aPXE6m9LwT3fhX0Aogazei9p6D3qOhZ2MRP1t3caxjCgkFFej8r0ZbuQePfS8e9qJp6lpIdSUBbhE7MGqbmdD6XQw5XI6x0c78O7uQmrqHnZ8bOK9R+T2XmK+IwlKouimO/dUH6dneebV7tu2TEpRCZlVmm+VPJkdzc3wYkdq2QiSzKpOUoFNP2VQVWPEP0qH312ApPoYc6BMn/wT+60Rtx1m7dm1zDpTo6GiWLl1KeXk5LpeLwsJCvvjiC9LS0v7Xw/j4f0JEeAjTHnsaUXLz0ROPogtUMf7GrhRk1rD+6xxOTttjaSp1H6pp+tGK6ALZP9N48FMqKpbidlezZ29rZzuvLFPt8qDWaolN74paq+Wnn35i7ty5raLT3IWF1C9YiGSzUXDtdW36WpqbTWnOIQCWf/BW25OpzYOt70J1LnzYkteksTGPvLzZNDbm4zhS17x88Y6FZKiGU+E1MnXvEbSiyFMpMbzaKb5NJeQHxqVxVmIwr7ZjhThOfn5+u59/C1mWeeJQPs8cKWHszhwcJ0VmNJ/H/kqch+twF1poWNM2NLy07Hvq63dSW7uF0rKFzctdh3NxHFR8OkofeZS8vDx27NhBdnY28+bN45P0BL5vMHFWwlDCH30c/wtuw9m5JcKqZ+cCNm8ZSVXVGpxOJ7Nnz+a1N95kwJTzSI4WOdItHWufOMx1SqK1IHURI4JMvNYpDlmWsdXVsnT2q2z+9gtkWWbm4WJW1TRwZUZe8zHqKxtZ9MYeVn2axXcv7gLg6+0FfHpYYtSMe8DPypHDA8g8MJptPUeiyfXi/mknksXSvA/t8HDoaCBgciKoBEWwFu4ASxmSw8P6uct46qmnWLhwIdIJkVQer8RVH2/ntRU59Hl2JXsKlnD+nsMsqWlgmUrkmZCzyFd15exKGy/G2nk1zo7GVIdLJfGN81se7fACWf45RNR1Id2lI1AS6Uw89XIx3Yv2c0lCLipUyN6WyJyhYecTGjmUwmGNPGM6xBXL4wht0JETY8fYqPiPfGfMZmvG2Wi8NYR2qSG4U22zxUpQSYzhFx4Q2v4f2L0Snt+Rbmt0/Gj2V+0nuya71XJBEIjSaduIzDJbGRuLN7ap5H0iVUUWQpsS9qlqS/CPbhu67OPMw1eLx8cZR6eUDoy87RE2vPkUrz36OI++9iIjL09j9bxDmEL09Bnfobmtv1q5cde4PYRq1WCOAnMURmspSfnfcMSYR/duLZErsixz4d7DbKmzMSzIn/k9lWgYm62t6VgTF4d50iQafvyR2PfebbM+Nr0rnQYPp/JYHuc/+ARudz319bsJChqoWAsC4iF1POT8DFM/bN7uQOZdWCwHOJr3OsOHZyI7vGgTzHQPt6DL3ALouDI6WKmy++WXyC43wVde0SrleKJKw1WyP51OLuZXtJOCvd/xcONBAgJjGdVjFOEh4fTo0SJkCg9m8PO7bxCRmMSkux5EFFXgdoBGsXCUzXwSnVOA8ecBLQ/+J2PoFIxtcwmeagf+A9uGbIaHjaWk+CvcnlrCw1qStum7dSPoyitwZudw9IJLKN+5m8DAQOrq6hgzZgw528rZvewYAH0nnIV5/DGWzk4g3BZMRGwCmos3YbfDvv3X07PHdmprawHYti+D2y7Rkp3zBX5+dZSW/4izUk+V+2Xe2mIl9LoOCILA3l+WkrVBCQsPioxmfGwnMq0Okk+o76TWqFCrRTxuicBwA4crLDy0UPG/e7+iGsOkq7m5NIdxvXugLczGvW0T+s6dEU3KTdDhcXDRzssp0ZcwwTuBF4UXYfuHsFSpDWXtt56dR/cjizL79+9n//79zYUKVaJAbJCR7HILsZoyen18D6v9OjKizyeYSu04LG56JeZxrbqEgiN6cpP8SfWr5L2ARAZllTA4S8Vj495kavlQ6iOnk1jmZpmulgOBl9NdyuFazbMM8V9FZZ2MpbyO5Cn3E1QeTBypJPVPxtHwAMtGFCM5VdSY3XQ6loBLrSKstozQ1FXEJ6jQBShTj35GHWJ1PDVDq3iA15mV2ra2zegQMzMPl3DpviM80DGKnuZfLww6Jn4MHcwdeGrLU3wy/hN0qlMHUXgkD09ufpIgfRBTkqa020aWZSoLrXQbHkNhSQVGj43oZJ+D7D8Bn0DxcUYyeGBPKitnkPvFm7zy1Is8+NTDWKodbP3hKP5BetIGKM6ZXfwNCMCWOiupfi1e/yr/KDoMmE0HrT8EtDi/emXY3ZR4bX1tSyn4yZMn06FDB1JO8OwXRJGYV14m5pWX2/TP45VwITL8ytvZ/M0Bdn1ZgC71KRx6xaIyetQRUKnh0m/abKvXxzbn5lAH6gmeplgYJxDFsUTg8EpIjse6cRPlzyiF5hp37iTundnN+9iy8AgluXUc3VPJjPcV64zUUIP84TjiBQ9fAN06xnPJ6EsYFjus1fH3Lv+JhspyGirLcVitGDe9ANveg7B0mLEV29atXFdYyJC9OzF/9AjPbnmSy9IvIy24tSVUHWIg8v62hfaO4++fxpAhm9ssF1QqIh95hP2b8vh2yyL2xqeQojIwd+adCIJAuarF2TeuczD+gf4EhEdQ2tgBfcLZpBSNoDp6FknJ9xASEsKECROorq5m+PDhvLoqi8MFFvIb4lhwYzrerRrcG2VcVQ007ijDf3AMCV17sHXBVwDERvpx/5a7uDthCOr+tzUf1y9Qx/SZA7Bb3ER0NFNvdxNp1lNqd+GI88Muw/ORaXity/i4+GO4ETKu+g8AFocbm6eBUpuSp2dp3lJeHPYi2Kqa968JdtPT24ENQhbySQJQEAS+nzGI/bk76PLjFWCHTjbFuiMHbwFLX6arV5NDR0qLw/Bk1XKb6lHcshZNzDb6Fe+m2+EArLKLL4abwCOhX20HGfaLqdikSYSoxxPSAUKeuJ/q917AkWPB0DOW9/aNY9PhNxH1hVzU4w2SjiXiDLkWjdRAX/8VpOh3gF5qdixJ9x9CUO3ZrPn0SzY8mYwxpm3RyBtjwwhUq3mvsIKJu3OYmRT9qyHHGpWG54Y8x3XLr+O2Vbcxa+gsQgwhbdrVO+t5astTbCvdxuzRs/HXtu/Mbatz4rC6CY0zsWePIjK7devcblsfZxY+geLjjOW8c8fwUXkF9Su/5N23P2bGHddhqXGwel4WfoE6YtOCCNKoOTvEzEdFlUyPCkYrnjAVEhALxbsgsEWgqEWB/3TpwKrqBmbEt/xI+vv7M3DgwNPql83pYdLbG8mrsjFR5eZwksC+1AQ08kzm0jaU/mS6dnkdq/Vm/P1PXUDTsr4I6yYRdXxfPAU78R/ROmFVfJdgSnLr0JyQOt26cTP6RhmVHxSoNcheA3fkG9l70kNtt9HjKMjYS0hcghKhk/uLsqJS8YWJeuYZ6hcu5JzLLmXilruotFfy/eHvybiqdQTf/4qjVmZbx24UB4dQHhSE96vLUNcfI+LyBVz36lBUarH5/CY9+hzvv/8+mc49OHIjGVP2BVFjFHHUv3//5n0eKHGxrWgIAKGhI3H3s1F1IBPJ5m6uaRTbuSs33vckGe98iOu5KdChEXXOzzhCBlH340rU/oGE3HgD5lAD5lDFUTrAoGHNfSNodHl4tqicr0preLNTPGsyW09DfL0slx1LN7JOr6FB9QK68O94clhTVMuQu8E/HCK6YIjvy/Bu3elUM5gDhw62qa1k1KqpXG1kfc0M0rVreb7PVQhyI1rjd2jTv+OlumConYKMAGZwO5QQYWuUCWOv87m81kLnS0bRd5ubGruLYNlAXu9QLh7SiCX7alBSv5CZl41x9S8IXi/uYzB53jVsOlxNrLETs0avYe0nuzio8gNVCGOtESQdtXEk0Q8E8C8eiKOoL9f3N7B33E3oQiO5uJ2/syAITIsKZmpEEM8fLeGJwyX4q1RcGt1WdByne1h3Zo+ezf3r7ufcH85laspUBkYNJMQQQrR/NEa1kSFfD2luv7V0K7Gm2HazzlYVKg8ioXH+5K3OwSVqSE/tcMpj+zhz+K9q8fyZ+Grx+DiZV557HWH/KqLOv5FpF03ip9n7KM+3MPW+3oTE+JNhaWT8rhyuiwnjqeTo1nPUuSsh5ddLMvxeDldYGfPauubvpn56KoOVH9ufzbPp0v1lNJqg/3r/cs5Kij/RcTwgJfK+dNShbZ9MPW5vqyrC7vIKci6fxrqwaF6Kn44nwYSnU0CbkMw2HFkDOz+Gs26BhNaOwA+sf4BlectIDkzm+ynf/+5zaVj+C0XzPkI3bRraIecQF2yk8GANBVk1dB4SzRNb9vFtgIFzy1fzn0NPKRsljoArF1FVtZrMg/cSYO5JVNQLvPfeB4BMJ08MIxP6Yx3lT3x8PBpNi9Pk4QorX28vYFKPaHrGBZ6yX1uuOJe7BhXQaPSikmXuyJExNQTSaXUtakkm6PLLsZ97E/UVjXQdHtNutWaAY/XH+HbX54zufA69Invzzl1r2WPbyMoQRXCMLNzFA7u+Im3XTkS/tvl82sPlsFN2OJfdvzgpzrGALDHtxf7YdDp+yp7D5wc/J1gbzIhjI7BarJQYSqgNqaGLTsfg6G2clfAL9R8pRf6M/SIo3FtOSFO9vcqk+eTV2LEXXEa9toKKgCy65+2na00F8U88w6YIiQ1FG7iu63Vo3phL3o9byex7B0KQPyHDvUw4eBuvRIlkyAIRBSPZIg8kyZVDpyP76TD9Cq4sng3Fu+HWLRDStvaaLMvcl13I9xV1bBrQiShd+xnGj1Ntr2bOgTksPrKYemc9AENjhvLumHf5NudbMqsycXldbCjeQIOrgWu7XsuMnjNQn5BuYMdPeexbVch1rw7l6XsfRrLW8dR/3j+tv4WP38cfff/2CRQfZzySJPHsA4+hLzxAr5seZvjAfix8dTdOm5sLH+yLX6COOUWVPJpbzLTIYJ5OjiZA0/QDlbsC/EIhulfTvmS+3F6AKAhM7x93yqKETlcVe/dejdWaRf9+izGZWnImyLLMRxvzyK6xcqtUwP733mVL3+GMTrIwZNLNGCNaTBYvbn+RhbkLeW7Ic4xJaC2UZFnmycMlLKyo5e30eEYEN13vh1dSm5GAbVsZ5jHxmMf8Poc+j1dip6WRHyvruDomlGTjqbM+/xayLNPgauD5b/dzMOsQfXr15MkLFcuF3V7Evv3XY7cXMmjganS6iDbbbz53JPdMqMRmEPDU9GJK7N10XF+PpymV/1Wv9OfDuXPxVBVwR8h6dNVZcPNGiOzG3n3XU12t+IqMHHGI8vIqLCX1JIRG89XqheQfywdg5syZLM8sRxBgXJcT8rLIMjSUgCkKTrSsLZrB0pyFPBiuiD7/RjUXro0BQJQkxmfkEfr2h8xfoEQR6f01XPfK0HbHZ+1nc9j1oyLc7v3mR358aCMHihezIjAOh6jj9dXvY3Lb6bjoB/QnBAs0VNkpzKohqVc4ev/WUSnzn3qYwoOKtWrCHe+jffUuXAczCbp0Opsu6cKTW54E4JyAsUz7sJZ9mq1kx8Lm8+5h5dDzcO10Ur9EKc1gGRTJ52XF3HJUEVi28vlIW1Zi9Ytm46AZ1Ifs5w4+Jhjl5t8nKRmXpJz3t68IyG43JcYQrh/3MLIMXRIcFBifbO7rd0OX8uPDLSH696Yr4djV5n6Y7/wFjaptHIbF46XX5kyujw3jodMsIrixeCO3rLwFnUrHt5O/pWNAx1brnV4nn2Z+yrt732Vch3G8MPSF5qy3y97PwGn3cN7dvXjqqivQJqTz8NOPnNZxffw+zohigT58/JWIosj9z87EFhTHjg9fITP3CJNmKE6fS2bvw2X3cF1sGG90iuPHyjp6bznI7VnH+LGiDlLObjX3v+pQBY/9cIBHvs/g0R9OXbujrm47Vqsy5XH48Ett1i/zl/jcLPFgx05M+fEH7g4bQ+fccdS8fkIkiLOez7M+p9HTyN1r726zj0qXhw+KKql0ebhkX+taP0HnpxA7a+jvFicAapXIWYH+PJsS+z+Lk4KDNThKBVy5m+ijKYYDPzWvr6pahc2WiyQ5KCj8pNW2DqubvH2VVA7vic2giEB18B5+LH+d4KaqwOEJJoTMg5y3eg1XhyWgvW0LPFkPkd2QZZm42CvR6SKJCJ+EIKiIiooitU8ndAlmHM6WPBk/78zl5s93ctNnu3hhaVZLJ1Y+Ca93hqdPsGZJXtj7FSMb7VzcYCE5IBVZq0JscpCN7x9D5CsvojGoMfm5kfES0TmIYdsOEblmLzua8nDIssyB4nqKc1pP8Qyb1IFzI89mkrGK81VZNEaGYrj+OnSprZP0/Th7H2u/yGbOfcoNvaTOzpsrczlQXI/d2hIJlNLZD9dBJeS29suvcFltOPX9sJqn4srUEqR2M/CQzNUrZT4ZdDk6XSjv8znfhvzC57E/M3X7Eb44amMGNsK09+HdvRIAf1sJkwyhnKcOxz+yxR+jV4Qi5AdFnIUqXJkSC7vk4mahEWuKJjVIOZcrgs4jpUMMwaPOYfHZ09g49lq2SulUyWamlV/Oxff9QvW+1eBunUXWpFYxMSyQX6rqOR2q7FU8tOEhhsQMYcv0LW3ECYBOpePG7jfy4rAXWZq3lG+yW3y/KgsthMb5U1dvwc9RS0THtpYdH2cmPh8UH/8IDHodM559jnfvu5MlLz/F1S+8zqTberDw5V38/OEBJs7oziVRIYwINvNpcRWzCypYWF5LfmgPNCcYCaMD9ahEAa8k0zfh1NMwIcHDCA+fgN1eSOcur7ZaJwG7G5Qb1YZaK4JWiypAh6e8ddVjs9bMtLRpfJP9DbOGzmpzjDCtmgsjgviuvJbZ6fHtd8RaCV6n4k/zR+NqhC3vQGA89JjWZnXujnJWfKyEA0ckBeCwtU6eFR4+noqKZbg9dXRIuLnVuiVv76XimAWZifRyH2BvRBmyCHcMHcHULr2x1TsxhxgouPZaHAcP4jh4kLAZMwDYNP9zti74mphOXbjkqU3kbC/j3VvWEtHRzAUP9EEQBKZMnMqmtzOJw0HG4p+5TlXIlvowIgNOcH4s3tX2nEUVjJ/FV3nH+CH6Au5NTmCi3zG2xUzD6xLp7T6XsvsexNDZytFRasUXdP0g0ot7kTNkDM8dKeGH3im8v+4oL/58iGBXV54bnUD34YqjsnlILJ2HxBJV25c333yTtYMHgdXKkydZ6jQn5Yx5/IcDrDpUwesrc9h732Mc3b0dQ6e+5NhVRD/3LI179nA0KZ7PV8xFF1mJzrGDH/p+xNVXxDLns/lsSO/Omiar4cbKzRwNVwSvuW4AdquKWorRCodIGKjlI1sYyWN1hFY+ivk/VRwRZeKeexX/Kdcy2+1if/Z+kuwyFcVXA6Cf/x9+WbySKpeWPglBZK67hWVfvo4o72G751syhk8mu7QGgK8qLyTIYWWA7CHaruXr92BGZKQiPE+gm8nAgvKatn+fdpiTMQeA54Y8h0Z16mRsAOM6jGNLyRZm75nN+cnnI7hUWKodhMWZ2LUnEwFI73pq3y8fZxY+geLjH0N4aCCXPvEM8x+/nzkzH+We19/gnJu7seRt5Wl01BWdiNRpeDAxipHBJs7dc5hMq52eJ9wcukQHsPHBkYiCQIT51NYFtdqfbl3fbnedSlAcbVee4GgbekVn3GU2NNEtfgaCIPDYWY/x2FmPtbsfQRCY3TmB2Z1PYSWpPQbvDgS3DUbPhKH3tN/uv2XnHFjzbMv3dkTKcaaOnIIxUiIqqsUkr9NF0KfP1+229zblpxEQmXPXEo5W5KAPDGh2YjSHKM6n5snnYtu8BV1KSnOW1+zNilWh+JBiOcjarETDlOc1NE/JbV90hF6o0In+xLh78ZnOwr1TP2D04AdaOjHhFeUcu0xt3bkBN/KiK4N6j5d7sgsZmrwflVZCpZWwlik1eDZ31bIaA/cu9BJTu5ezjzi44EgF3R++AVAsHgA12hB6X3Jxm2vJbDbTsWNH8vLy6B03hk8e2MigC5Kbo88m396DsqP1HFw3j1envYZx+K3N2wZGRBLSfzRnv7Yel1fiuiHpPP7sBax76mFcppZw+D77NrIs5VK+HaSko19bY+H8iCAGp9xFdtY3DIkeR1jxbDwlsUQ4K/hI35sbOu7mo44q3h7+OuZl2cAbIAm4HX4giiz9aSn79+9HkCRuuvxypCObiU7YCP9Jo8OVi6h6PwvVm2/RLcRMdlQwQZ98QYppGdxwFwDaxgqcGjO6SBNx0kriREUoSZKMeEK8ulEU8chKkUvxFNOsoFiqfjz6I+cnn0+wPviU7U7k2q7XsiB3AeuK1tHVqThQh8b5s2ZRNl5EenbzCZR/Cj6B4uMfRWpiPKPueIT1rz/J6488wSOvvcioK9NZ+clBTMF6+k9SzL89zUbCtGq+K6+hZ2gqFGyF+LMAiAr431PYjw0NaJV1VNCIaON+vWZPe0iyjNfh4IcXX6Egaydn33gL3ROa/i2t5Yo4ATi8EnnQnRTddjvWNWuIfnEWAVOmsPVoNffO30fXGDPvXtYH1Qk3AbfLyYLnnqD4UCbn3vMIKQNaO8ASdsIPdXh6m76l9ItA55ZQ7yrDWGAjsHdyS7r232DSbT0ozKqhY48wNHoNafFKbRWHo4SSkvmEho3BXFFJYP1HBM5/Grpf1Lzt8CuuY+ePC+l9jlKVufe4BGx1Tjp0C20WMZWVGTikNHRN/enbd3HbToR3ggltQ8QBro0J5bPcQzzkV01k+Hmsy7EQEWgiov+F7KpPxOytZMih+aSWAEh47QeJry+gdsoSIg9lcd+4NGKCDPRNCGpX6KpUKq666ipkWeb929YieWVWfnKQtAGReCUv2Y0HiYuNImvDagAS1r3L0te/JDFMEbhWpwdXU4K83AolCsVyzkWELPdQFeogpD6MafYqBkQFs63OSrBGzcQw5Xr8pTGaupCbWeqQebl8Kj1DI9CIOr6t/YALqh5g3xMP4SnL4qXS1UQPPAuXVsvNTYVcj7skekWRH6++ka6Hw4heu1E5qSOrqf1KcQ5PqG7APX4kzlU/0LtIoP+2qwitCSDymOJ3UD/EwW5/LbvpRDqrONkV1tvkAf5bDpDritZR56wjwtjav0mWZWYuzuSXzHLevrQX/Tq0iJd4czzRftEcqDpARF06Ko1IUISRqvyjePzCMOh9xWn/KfgEio9/HIMG9KDqitvJnvc6Lz81i4efeRRLjYNti45iCtaTPigKrSgSr9dS7fJAUAfIXtosUI4jezxUz/kYQSUSfM01rRKh/dnk5uZyoK6A3fP3E2npgcqZSJAcwIr/fED355SpDmL7wZR3wVFPTtAIds2eTc81itNoyYMPETBlCp9tOUZxnZ3iOjsNdjdBfi23gpqiwmYrxOLXX+Der5e07kTK2djPnk/56+9i/HIlYfd0a+U0LAgCAdV2rCU2bCU2jD3D0SUGcDr4B+lJH9Q2eVvGgdtoaNhHXv7bjMqJQijLgPwNrQRKUp/+JPVpCR2OSw/m0idb/+3GXjSAb158lRRdB2ypAfRLm0Z09KRWbWSPhG1nOepQPfrkE6bzvruOBw98x4M6MzgbkFapecw+D4CLdK9iFfYSVm0itKY/VlMGfhYrAiC5rGiaSnYEGDTcPPy3fRnW11qpHB9J0NJSBp6bCMArO1/h86zPAXhn3A1krfqRsVE5BNyXhjV1Mvq4Y3RJO4ePr7oEx779pM6dSeGuJIY89wIv2C/HLsl8kGqmT7c4VAYN33SK5tj+PXgbQ8HfxMWRQYTtsPHAETeyuhMBTRFIw/snEu+0If7yONqt7/AY8GTC3a1y/0yaNImUlBQyTCHcm1uMRurFFz3vZFhkFAy4GdeOOTjnf8qxDiPQ9/2BysFu9HsFXvwPfNe/5drrX53Jzqby1g2p1xJ+krDNtDpINOhQ/Yr1BBTnV4DEwMRWywtr7MzboiTzu+j9LeTPal2mO9QQisVloarQSki0H6JKxFNZhDrsT5gq9fGn4RMoPv6RnDtxFB+Xl1O7/Atmv/kht991I5ZqB2s/P4RfoJa49GAyrfaWGjXJY+DQUgiI5VBeHblbN9EtugP2118HwHn4CNGzXmhzHK/XSV3dNgICeqFW/34Lyaxlh3h/3RHuG5vKbaOUG0FBQQFffPEFZa6fGFhyG1YTeHVRPB0RyNXd+gFNVhNBgF6XAfDL7NlU1dRgSEulm9tD9IuKT8sl/ePYdKSKtAgTppP8GsI6dKTnuImU5mYz4fb7aY+qz37AfiAH+4Ecwm6/DbQtN5mioiLWZa1hGKkIgoAmpnWYbFVVFZ988gk2m41bb721Tb2tDEsj5+4+jF2S2DWwMzF6LZLUUl/HntQP7bEDFGyIxfl1Ogmff4axb18sm4qpX3IUTZyJiBk9sW4tpe6Hw6gCdUQ9pAiX+K49uHfuJ9gaczEaO6JqJ9uodXMJ9UsVp+XQa7uiTw0CjxMOLFAaOJWEcKLsIVa1hoQSM9duUaasPhgv8v04kWUp3Xip9igdJl+GOnkS/h06tDqGLEkIJ0QIuY4do+iuu5FdLuRP5zEtowD8ocu10dzeT9m2vLGlyvbQCaMYVvA4ALZALXLJD4ATCrcyauadlMxdTX1+Pu78fJIfrOXwsO7Ufn4QR2YxpT8UEztrKEvffoWju3cAoH1pDrMOl7B2lw2DF9CoOObnYlWMRKeYvXT098DRsObj33333UhLl5J30cVo77iD6CGD6datG5u//Byiu+EWNdQOvJvZDhcp1Q3EDZ/AloJEVPp6krRKsjtHN8UOktixDKc6AE2jmZHswA87atcNBJ19Tqsxc3glFlfUckHEb4fipwUpkU/CSdV8YoIMTOgWydKMMt65tHeb7eqcdRg1RqqKLEQkBmB3ODHaKjH3G9amrY8zF18Uj49/LNdeOx16no176xK++PIHhk9PJa5zMD//5wDHjjXgkGQijhcWC02BThOQXVay5z5K9Z7tWLdp8Z/yPoIhGNOY9ut4ZB16iL37rmHd+p6n7IcsndpQ/fFG5Qb5yi85zcs0Gg2CIODRQ8f8xZjrj9LJrlTtXZjb0O5+unTpAoJA1eTJJC39CUO3bgAMTQlj7xNj+eamgahPCukURRWjr72Fy194g+DomDb79EgeSgYroknbsSOcZEHau3cvufYi5uhXY73UTHV5Uav1+fn5zSUCduzY0Wb/a2os2JtqzCytVJwku3Z5C7O5JzHR09GPfAnrgG9wVnkBKH9eEYiNeyoAcBda+HzWdkq3Kj4o3jpnq/0fzXuV7TsmsnZdZ2S5bb0g0dQitkS/putArYPxs3CG9GJ/yBO8GJLALYYJ6ELXkiIsxx0lkfGYmZyoa/CUT6YoqpL7TTczZ5+TVz/9lDVNFiyA8hdmcahzFwpuuLF5WcPSpTizsnAdOYJ3+S8YmsRLul/LtOLD/e7jivhuvNR1CFJYMlz6LY2x11GS2Rm6nK80ClCcps3nTUEMCECXno4mMlKxOJxwqrU2FzuOtkSpZVgaiS0rIk+rCEHRrOWJUSG82zGMO7Sv0yXtdaoabqDBcxEHB7xGri2bsueex5GRQcMNN9B1UyZ15aXYFn/DTZ+9zC2fziLf7uLZo6VclZFHaZofk+/ogWHQakRBBhnyPh3B0XH9iBxuJ2FwGdY+Hm723E6142oS5E2ogltPga2tsVDj9nLZryRqO068OR5/jT87y3e2Wq4SBd69rA/5syYysXvrUOUyWxkFlgLSAzpTU9pIWJyJfRk5qJBI7tQ6msrHmY3PguLjH83dD97Ocw9WUrL4E1ZEhDP2+gH88Noe1ryXQcAwA9aTCt0JCYMgZRwd8/KIMJTglMIJf+hDTGPaT33t9VjbXX4c+8FqqucdxKu24bkqh8DgXoSEtOTMuHdsKh9tzOPhc1r8PaKiorjpppuw2s6jNOg+Bhc/wh2hI7jYs5UnugGS8pQnyzJ5eXkYDAZGjhzJiBEjTpm35deoLbOQveMQaf07ExTRYgV5d++7fCh9Ag+r2XDBF9QtOopk9xB0YSpovJjMq9DrNZhNESx78Skkt4u+k6cy/PJrAUU05efnI0kSY8eObXPcSyKD2dPQiFmt4uqmFOh+fon066tYMCrnZOA4pEGTMBB1sIPYt94EIGBsB/Z/eZDKGjf1dVZ+AS7qFYqxe+tkdY32Y7963ns7GNg5JZopSeFow5vSoNfkwcbX0VnLOFh5OSZhFjt7Po1D42TBcCeBPbqQ2ZDIwVrFJ0dT05cOmnzE4mIICGDjxo2MGqVE7DT8/DMAtg0bmo9pGjee+iU/IjU20nHieNYfsGK3OUlJaclmLDdm0EfeBvWwf/VVhP0UjTaxCyEzrqTyh1cpK+9Hwpdf0PjTUkruU2r3dNywHqHJuhV8cRr2QzXIkUZWbyzke9MIOpvDOfusfdz281NoPtpGpT6Ay859GJugJsjeACrlfIy2VCoL7Ti4Cs/+AxxRXUrYIyYin3Xz3YgJdCqysHLZVuJTzqUg90cm3fUAGSfo1jAkFq+dT1VFCAfCJjCu01Kc2tEUYiSmohJDaA6pwUdJHXWUuwqNvLl3EmGNHgwniMXjl3Cw+rdvP6Igck7Hc1iYu5BrulxzynT2J/LZwc8wqo10V/WjQMokNM6fHes2KVXPe3X9ze19nDn4BIqPfzSiKHLfM48z6+772DXnNcJCn2XijO4seHEXV22wsTumgctPelI7975HcdU2Yv1mI34NezD3HXSKvUN6+iwqKpcTEjy83fWN+ysBqE5cQm3hz1AIA/ovw99feVK7aXgSN7XjqxAZGQlE4r1sNlOWXMiV9du437ocdgDuKkg9m6ysLObPnw/ABRdcQLcmq8nvwW6xsPzbuwjrspbdmRBmzKDE4WZsqBm7pyU/hTu7Huf2MgBklxfNVBcez2L69QdRMLB7fQcAqgpbRIHBYODCCy885bHDdRo+6dY2Z8VxPBV2BJUGfa9riJ3VIur0qUGsPyuE0h8LSURFr3EJhJ/fdgxTUx/H3y+VkNARCEJr61Gju5Ebdq2iRoznhUwLZeE9lRUFW8CqnGdvUxGr6zqSXNWbA1EbCLF0ZH7dJEakbYACpflFukzC66oYGBhE3cCBDB/ech1EPPoItV9+Rcj11zcv0yV2JGmpkivGVWRB+DEPI1CeVUvkvX0BMJu6odNG4HSVE7wxAsvPP1MxpjNlxZ/QfbdiKSh75lnEE6bbvFYr6mDFEVQ0qDH2DOOLJ7ZSX2nnNoKwjqokRC7n4GovPYAwRz3vz3+Ic857BWv298zsNI9E4SjamIPoe4dRnlFAdtxiQtUycmQDnzw7m8xyD/fvPUIKyrSK31mbqS0t5tgbs7guIIT73plDZd5RqiqUKSpneSwv176FqU8AIzNc5P1yER7Hd8gC7B9/jD46NxXxHdAZW99mqlweAAJPkZ33ZK7rdh0/Hv2Rp7c+zayhs5oTsLXHlpItfJ71OTN6zsBa4kEQICTGn7KjR3DpgggO/P3TtD7+PnwCxcc/HoNex+3PPcPse+/mp1eeJvj515h0ew++fHEH9d8XUZ4aQ4TxxCc4AV2wH7pbxgHjoL5YyTgryyB7QdRAaDIEJqDVhhAbc+kpj+3XJwL73kq0jS1RBiemuXd4HCw6vIhOIZ3oEdajzfZpwWk8P+R5pJLdyGveQpA8EK8IJklqsf54vd52j19+9DA/vfUSfkHBXPjoM6jUmub2DoeDPVvuxhDckgRuwq4cPDL0D/Bjfu87SQ9Jp2tIV4KIojKwAm+dE9PIONT+ekJDRlFVvZpeveYSd78Xh9VCl2GjfuOvcXq43bW4J2djKOpEUP+2wuvecWnk9IwhPtiAQavGtm07COB3Qt0dvS6SxMQ7293/dcuvQ1V9gFDUBHb6smVF+mTIW4/stuOWz2bADieB3nguzLmHw+pScuV6PgmewZV+yzB4vYQaC7nu4nswdOnS5hjmsWMxt2M5Oo4qQIdoVCM1ejD2abk+9LooBie9B8EdsWn2U7tgDfcPjafj4Vr8omNJLCnCGjeSyPREpMZG/AYPRhXUck1Za6qpyD+K22pF52jAodVyXnIf1u6o5PukwbyUn4NGkqgwBhPlEehsDaZ8Q2eSEBBGisgjQ/j24NeYyzvgH1yAQ4hj4Q43XjwIai0o+gFd940UbFSmT4LrqwnSqPHv2JGYxET2Hi5ntSuZur7h3LK9iDjt61jsAnrDQEREPCXV9OlbRdeRnRBPmnrcWGuhi7++dd2sXyHGP4anBz3NgxsexOa28eLQF9tYUmRZZkHuAl7c/iIDowdyTddr2PzNUQIjjGi0KhxlBYghbac5fZzZ+FLd+/jXcCS/iC8fuw+v1o+7Xn+DshIXy9/eR32yH4/e2Q9VO2m320WSoPow1BW0LBMECEsDc0yLjboJZ3493gYXUmIDen0EanXLj+dbu9/iw4wPAfhhyg8kBba2BJTmZlN4MINuo8Zi8PMDQYQjqyB5DLIsk5ubi8FgYF7xPL7O/pqrOl/Fff3ua95+5Ufvsm/FUgCmP/MK0amd8Hq9fPDBB1RUVDBo8A/sO5xEoL+LRibzatwAXLLM0CB/vu2Z3Kovx38KBEFAdrlApfrNyCaPy4sgCqjUJ1kwGurR6HRodHpkSWL/quWoNBq6DB+NIAit0tiPHnWk3X3LkowgCti2b6fgyqsACL39tuaEbr/GyPkjqbIrvhntFjk8tpljGasQdhaTVTuVw041wSqBMrEBjdvEd4MMdAr9mcsDVjF40Ib/amqtpsRGfamVuORA1AGKE68sS9jW3I928xy0Hhkeq+SjufMoKipCcDvxP5xBQvw4zlL1BEA7VSS8/+DmfUpeLx/edi32ykqGZRdicHsoiQylMdnFoNhSHnNcR+rh7YQIg7AEDwBBpDJy/fER5dpr4zEak9m3bx+VVVWcNWAAWxes4jMpGpAZEpDBCEcC3tgteI0ygZVTKLHnEN+zF5FJKTTa88nJeQqXV4W/cRqbq0qIqM5hUkMWq7xjaJAGMhg19aoCYi/uhtEQT3PpY6DC5eaeQ4VcFBnElPDTr1flkT08uuFR6l31XN/tevpG9G21vshSxLPblJw+b418C61Ky8ZvczCYdfQaE8e3Lz+LLrUP5009Hu3T0qe2HL8lKut1+ij8/VJO0dbHifzR92+fBcXHv4akDrGMvfsxVr/yOG888jiPvP4yMRd1RPVNHs+/uZObr+9O2K8kZ5NlmXcKKjCrVVwRnYIQdoJDneSFqhw4vLL1RiFJ6BI6NomWME4mQBeEw28wguTCqDa2Wid5vXz33GO47HY2fDmXe7/5sdV6QRBITU1FlmUWrl0IwKcHP20WKM5jDSR37MfhoK0YzQGEN6XwdjqdVFQojqaZmQOYNDaUKP9p2DX5dLMdRA4cx6iQtjeH4zdh+7595E+7BIDEpUvRJbY/TVNdbOXbWTvxuiXOv6830cmBAOTv282C558A4KpX3qG2tJiVH70DQFVBPiOuvB6V2PJ3sHi8GFViq5DT6i+ysGdU4dcvEl18y/K62nr0NQ5MwXokux3rxo0Ye/VCHRpKUf46bI6tqKIu5eEh71BQvZnzks/DlZ+P5HSiT0ujyuokq7SBgT9cS7i1mh3uFEpcSg3eGq+MUWfGohYojTHy9ZDH0Nvv5vDhwyQlJSEef+KXvPDj3ZC/EaZ9BhFtrSuNDS7mv7ADr1siLN7EhHs78eXCzwi2radn6blAH/wDX8K6eifFBWUggqxRRIyrMRqaZiKyqu7CzzafPa5QgjRqOuk1eFwuNF4Jg1sxdUSXVZE5xg+ds5H39K+xMHYq3vQf0bl3MvryD3j4o3LE+hKmTjoHP79qQkKGEtq/H9fsysWVJ7MqaSSPPfEUfm4HXXZswylpECSZ6pe2gwQh9CL2RmUKzlOhOEVrVV7mZyxhrcELKlikvZGYks5s8s/hM2tH3u4SSlzs5W3G5aED+ZRoI5iWmo7faYb1y7LMgxseZJfFwUP9ZzI27eI2gjE4WGacpZG3dr/F3LwdPNjvIcoOSvSb1JHyKg/2Aj2xI8YREvL7o3iqq9f7BMrfhE+g+PhXMaBPVyqvvJODc1/jpSee45Hnn+A7Wca9MJ+5j29BPyicyyeltMoXAlDpcvNYbjGLKuoAJerkjoQI+gf4oRYFJEFkvz6BHxrMTIsMJt3foFhaao5C7i+g1kNiaz8Vr9eLIz+c8zMHE18azdeaOu4+vyXiQBAE/INCqLEXYQpVxE2j04POK3HiT7cgCNzX7z7mZ8/nrt53AeAus1H5/j7UMlw86lGCL24pRGc0Gpk6dSpFRUUMHToUk8lEQ0MGB3cq9YDMdd8hhC445Rg27trd/Nm6du0pBUrFMQvepqJ/uTvKmwVKRX7LlFL50cOExMTi1SqCJCIxmV+q6gmOmUn3qPPZ6k4hZYNi4Tg4pCvBGjWyV8aeWQ2AbUcZQRcMxf+Be/li/0qs3kZivrqZMQNexG/Bm9R/rxTq03yxiryqWxA1Tij5D1cKC5iZdC6mknqOTDkPPB6Cbr6Zi1zdKKyxI/Iy+1XXsisvmD7hjZR6/UjSiYRpJeKfGcZ9ahGPx8Nr//kPjY2NGI1GHnigKUttWQbs/lT5/N4gCu89gN1jb65R00zTg7jklbj5vQfZ5gzmA9v56F1KfhiP7W0OHt1KuJDAtiQPWSGBlA8Yy3dyFO7IVRytfpHYMhuHty7gQmE0CAJzunZg3C13snvpErTjpmB2uSnqkUOQdxnbZDP6VaPQ2SG4pIrGIRVkHHqSFVUTATOZa0tZlKxMAa6rseBqspht7nMW165fiWgyIahUGFFEga5DAM6j9Rh7tgjvHV/updoSgVpjQFsADKjA5DJxTmkUZtNLPOAu5r74UDY5enHyhOa2OitLKut4o1PcaYsTgEVHFrEsbxmvDH+FcR3GtdtGEASu73Y9/hp/ntv2HH31g/C4JMLiTGzcvwmAnj3aCsnT44yaZPh/hU+g+PjXMemcEVRXVlH101yee/AJ7n36McrSw5i/MBt5XTkfb6qgoasZc/8wvAEaDlrtrK62YFSJvN85AT+VyFNHSpi69zAGUSBYo6bO48XmlVAJsKiiji+7JyoiJTRZeZXuVyJEgltu5tnZ2ZTu2IIRsARYMC8X4PyWjK0er5fEc6eRKnkZNGo0C7cf4qclS+lAIV0G6ZmQ1A1tBzOiVsX0TtOZ3ml6y0me+ADZTnbX7t2707179+bvWm0IKpUfXq+N4OAhvzp+gRdMxZV3FHV4BMHXXH3Kdil9w6kpsaLWqeg3seW8e46dgLPRhjk0HGvpUTYum8f++EGMUu1ls7WRRzOU0Ov3O/dml7UldXu+3UmwRo2gEgiamkzj/ir0Y+J5p6CCbZZv2NOrDMjk0pLxVB2px+jxNG9bX+nEaY3EEHyMYyjp9LNsdiSXFZraOY/lYwtWorUkRKyjP0Y6/AGV1o30DR6FVtQjyGo+uWMt5gQz597dHU/Tto2NJ9RZCusEiSPg6FqOnf8O5/8wBbfk5tqu13J3H0UEGs1aet7ehe+ySumRZqVmqQld+E/sqpJIrVYEyseUMcS4nut1W9gV+TDHwpRCfYl2O5vnJnHM9QEhpo9JOvosY+OWk2cYi652Env2LqYwez+Fmfu495sfOZb9CvtyVXQ2ePGraiRx/R5AjWGXRNScm5jaSyKr1ML7l/UGFPE5PSqYDEsjskticqAJlam1ZdF5pA7J7SVgYkdMQ2OVwosbXyelIZTdacPQ1sqEOl1csTUOe1gHSlTVlNincgmvM7+kjIRB93Arrfm+oo5onYaLIk8vZT2AV/Ly/r73Gddh3CnFyYlMS5vGymMrWbz1FzoxmrA4E0ULc3Gq/YiPbVtt28eZjU+g+PhXctWVF/K5SkXxkrm8fPONpE++hAduGU9ehY0lPx0haF8tqj31HIvToeoRwINdI5keHUJwU8G1MSFmdjY0srvBRp3bi59KpIfJSKJRxxX7jzJhVy63xIcxPSqEOL0WorpD9rJWAiUsLAyNRoPb7SbQEMPUW1rPm+/cuZM165TU4aawcJZt28wuTxz+QiX3rLPQf51SbfnECJfjaCL8CL+1J5LTiz45ENntRnI6Ufm3H4ap10czcOBqJK8dg6El5LW9WiiqgACinnnmN8dYrVUx+MK2pm+twcjQ6VdxcP1qtq1YwAexl1PjDGKjN438rdN5dLhyzlpRYEZ8BE5JprfZSG9zSwi0X99I/PpG8l5BBc8cKSHK3PIUq9Va6RysxX/mTPyHDsHYrx+EhCOteh8/fwuW+I68DVwYEYQgJBD77jtItkbMkyYyv9LKjvxaJiSaKBs7hr4qOJJg5qZQF3cKGiS3gF0Ce14DWq2W66+/ntLSUjp3PiEMXaOHKxcBYK3OxL3XDcCRuta+NFdXlFEXIPF5mZEvPd/ygqc788KXsMNdxM76C5EEI7uEkQTKhezbE0pH9vPq8tkUX34Rx1x9ANhkuZYtmjD2qFYSWfEL2cfi8VN1xqs/gsphQ5ZlHsn4hVKbMj20ZEwttbUCeLxYBg9g+0t30kmAEWlXsGb2XuyiP+Xyai677DJG2LQ8+v1BVnKIG4Z15M7RqfjrlOu/YUUB7kIr9YVWTENjkfd+hQCkmau4tmYTsc5yHCEin7j64W004vRvSdL3vCqJgJojQP9W46ETBPxVqt/MHnsiB6oPUGwt5rkhz7VZd6LP1HEEQWB6p+l8tW0VhkA1en8NtpICCIpqs/3v4XiJBR9/LT6B4uNfy+WXnc+2TiksnfMfCr99l6d+/oEu489jxlVjkT2Qs72c/asLqV1SQcjuRspGeTH3i0CtVSEIAv0C/OgX4Ndmv0t6p/BqfjnvFlTwWn45c7p2YGJYoJIEzGkFnSISwsLCuPfeewHQ69v6vgQHtzxJhoaGMrmXlS3FYdQKigOC068Et6ESWR5MeWMFeyv2MjxuOAa1kvRLG2dClmXmb3iOqHsXE9rQQPDNNxNxV/uRLTpt6zwij+QU8XFxFaODzXzRI7HdbQAklwt3URHajh2bf6S9ksxNn+1iZVY5L17QjWn92lZjDo6OpVOHKvxNjdSUKT4vJaE9+K5nEkEaNV38lfN4KS2uzbbH6WhQbrzlMfczuGEeCWWhTA29HPPIeAS1SMC55za37XeOMsVyckYb06iWyKPkcBPJ4Sa89fVIkkR4o4PQ6gr2XBDD7YeLiI3yMjoniPEDY5FlmfDw8DYZck+kS0gX3hr5Fha3hUmJrVPtG1UidR4l+mqUvZroyfMY/8YGclw1RHnLKDbEEFrj4S3TDCrVyvjkdQ0iJv1L4qxGCks6AQJSzWQGHvyFEXmTkfocAqDR2ImZM2fidDqJK4tDh458Uz71MeeyybsIlSwTJSWTW6mmShtN310twrk01criX35BTD27edm89XkcWV/Ih4+ORPATqe3yE3Z1JRGNl1Cf8wtFwnBcqiPc2XgFNZKWqiGdeNDxFN39DwBH2DW3N2d1LiO992LOWziXqw58AZecYPEDDCqxOXHf6ZJdk41aUNMttHWkl6uggPxLL8NbVUWHBd+1irLqGd6TFbZsxDBFOKrrStB3OXUqgd9CownG7a5Fqz19y4+PPwafQPHxr2ZAn64M6PMWS5evZ9t3X1L47bu8sOhzgvuO5OJLL2T60AEUHapl/5oi1nx+iM0LD9NlSAxdh8dgCm7fodZPreKJ5Gguiw5m8LZDqI7Pt3QcAVmLlZo/q5+FPZ+hu+BjioJseNwNJCTciCgqvi9WlxVdpI4777wTnU6HQa9nYuhtRIXvojbLwpv9e7A3aywSThp2ruTJQheFlkKgdVTKj6UFHNiSS7cGJQNtzfvvn1KgnEhxTi3zj1SAXmRVTfvZa0EJdS64+hrsu3ejCgsltSkpWXmDg5VZSj6MBxdktBEobqeXsIRkgjr34Trxaz6Mv5EQjYxm7EqG6E4uHXdqxocFsPWsdMzqrgRrftvE3x47ynbg8roYHNMSDbOsegPvXu4mulrkwRteI/CDuQzzGtmSWENmejh91X2wWsMxmU7Km2GtwPLlhdiqDjG793nMHPsOI+NHtnvclX3T+CznIBfWbIT7cunkb+aL6wdQmp9PwZynkSUJyT+NAq+GQEmLv9eGPUxL3bEe+FfkMMS0iYzGCQw0fUbwgSQaeh+lrl85PdiNWJKI41At31UeojDgHFLLCnl04EB2ZX9Ag1FA51bTcdAo7q+txS2L1DpdXNcxku+jrSxtchR97pdKooKMlFjtPOzWMwYNpc9t4+XRc5gobkCVBHJwLX6LziKg+goAKqUGzrVoCVtUR8T4FqtWhKqaX6KGMXj7MkJrK9sdD4MoYvf+PoHi8DjQqrRoVa2vmcZdu/FWKVFatfM+w9BU+gHAT+NHqC0GVWc3x4pKMXgaiU1uHbH2ezAYEmhsPOITKH8DPoHi4/8FE8YNY8K4YWzaspfV3y+gcfMSPt7yE97E3pxz8UVMvLU79ZWNZKwt5sC6IvasKCCxZyjdR8YRlRzQrnn3eIb7oOMJp0QRupwHu+fBns8AEBZcS86wUCqI4PUSmRu6X0eqUcXUxVMptZUyJGYIs/u+wJEp5+EuLMTv2efo2T0GKUQPogwSyCfUrzkZP20gtSnBvNfjXKItNVz1wbOnNR6HtpYxvsDGriQ9jzfViDmZ6upq5syZw9gDB9AB3sqWlOpRAXpuHBCBZf8v6GQXpaW9iYpSzOjl+Q18N0tJOHbRwy9zUbBE7qpCVJJAoFqNq8RK5Qf7kZ1eoh7uT55UyB1r7sAreVlw7oLmHBd7Gxq5IuMoIrB5QNtqyx6PjZLS+ZhN3QgMbJo+k2XI+A5cFuh9FRnVB7l2uZL5dnqn6Twy4BFAmY4pDBMQAz3s/voNAvMrCARKHWPoGlrHrmO72LVrF08++SSSJFFy551YVqwkdsZQTNX7MAE9Mn8iu8dtdIns1u71EagSmGYwEDrwBtAozqmDk0MhOZTqTrNZs3Iln+R6OeQO5+yq1XTzFDIw4nLsS37GP285CaOq6BH2E4eS/ZjnfogjA7rSICg3ySUN31M97yDvDdRyNDaZ/bHJhFfMp656KwlegVHnPkhsTDwGanEDBWovBQdrsEfpQZbpUOHBUevkrsxv6Z6zlqKek6CDYo1aXbKL1Rh5KNKOWNcNUfZwPP6sq0tFlGCjIeggBXv7UxiSSnVJGKM6/EgXzVJ0i9xISS6uGaPn3pPGw6ASfrcFJUgfRKOnEYvLgknbIhZNZ59N4/btyG43kU892WqbgpISDB4TwbFq9uxRimV2+68dZEGjMePxnFrE+/jz8AkUH/+vGDywJ4MH9qSgqJwFX32Hbe861rywnUXBHek+dhLnnT+G/pM7kr21jP1rivj+1d2ExvnTfWQsKf0iUJ+Q/TJArXyucntaHyRpNHSaCHkbcE2ehbrmFeZ4buKAqwfzd+aQPagjFY1KGPDG4o04g47gLlSsI1WvvMrT91/Fa9kV2EqeJ/ksmbTuU/iksYo9lXsYHtsSKeSyN+L8Zg6p6hRGvX8pXYJaVxpubHCRuaGYuPRgIk+qQtx1aAxlc+s5p1bH+I7t10QpKiqisbGRDcOG0sPjYeDMmc3rBEFgQkc1P+xTxNPmzZu54IILAKjIb/kxP3agmvwINZ9vVXLK2N1eHgsJQnYqUx+NB6pYZlzWbB1aXbiac5OUG+X3FbVUNmUd3VRnZWxo63PIz5/NsYL/AHDWgF/w80uCwu2wsCmza/bPqMY/2dxer1IsYo0uD4NCLkTVXcWkxYux1WdQQCQiMkWGGFQuG16tG6O1jqNXXY1z27bmfZR9vJbCi7R0cLtZbYmm4K5HWA7c8/WSNiJl+ftvcnD9aoBWIeQ5tTlk2jMZqdnCUtsEBEHHz2FjuWjcHZSxjzTHBCxHBPJXhtFh9wryt5+Hp7wWS5PIAYipGo+Eh64NAkeb0k08se0dthSFErBHi3T4Bb5d9gPBETdynkNDqSjRRS8yJEOij8GG4ZgLZImuuUqOlNi9P/LYOUMp8r7KlEAX8VqJI4Zx+NUksdm+m9jATZSljafPOg8OQz6SysVmRwz7CxTHXrHSwZXWroij9yP/MJ+effri9XpRnRCtY1ApFpTf48/RPUxx9N5UsonxHcY3L1f5+xH9wvPtbrN5234k/OndvRPz5y3AJWrplJJwWsfzcWbhEyg+/l8SHxvB3ffPwO64ngULltKwZin5X7/Nc99/Rkj/UUy7dCpdhw2g8FAN+9cUsXreITYvPEKXodF0HRaLf5COcJ2GKJ2GTbVWxQflOAEx0PsquPgztKKKwZ5z6ZNbzoEypa5PgC6A2aNnk1mVyaXpl2JQGQm56SYW7j7Iy9Ovpp/9ANG7buX7su85tgc6va8iwi+C8X7jW51DzrbNzTfAQq+DLrfe3Wr9pu9yydlezvYledz01nDU2pabRURHM5c9ddavjlF6ejoFBYqwGHTOOahPqp2Snp7O0aNHcbvdTJ48uXl5p4FR1FfZMfhr6D0ugeAqGya9GovDw/T+8fgFGnEdsyDoVPgPiGKSbRJrC9eiElSMiR/TvJ9Lo0LYXGslWKNmeHDbFOVaXYtvSHNyPL9QJeTb44D4Aaj1iTw2bA4pRjW9I3ojyzIXvb+FzJIGBpAI9feTk5CPNdFJbbE/Klmgqt5JZtcEVKHRDF79PscN+7q0NKKeepI9YY18XLGb4T+VUMz+Nv1yOTzUV9ppqKxos87pdXLVsquwuq10LJzMfr0W8DJBbUPXkIAc2kjkk08iNdoRjUZ0chDfu3tTGvIdAaU7uHf4G1wm1KJdcw2PpL7Bz1H+XF6ymJdyX6NeFJk3XMemoSKvmhwE6goZtkwgwisS4RUJ93cRoPqFobWN7ORcEESyYzuRJjewe/gIbnVVIYePwWqaC0CS52e8xikEhUWypNdQNjq8MCWIzfGjWPj916Sr3OxvKsw8UtcRsyocGEOdegHxtngkSWolUPSiiAQ4JRm96vQESoI5gd7hvfk442PGxI9BLf76Lctit1G+xQPRFUSFhVOTvR+CYlty2PzX+Bxk/w58AsXH/2sMeh2XX3Y+XHY+azfuYP3332Pb8AMfbVyCnNKXc6ZdxKQZPagrbyRjXRH71xSxZ3kBib3D6D4yjgvDA5lbUs1dCRGE61qecInoCsW7Ia4farWJFzr5c028gySD8hQ/JGYIQ2Jawn3D776L+nW59D28glkVb1FpUPwlknq3Tf52nNhOXfALDMJWV0vvCVParDeHtlTQFdoJRf4ttFptK+FxMjqdjqlTp7ZZrtGpGHJCdE9SmD97Hj8bQRBQNfUj9OoWk3vHgI4sOLdtXpY0Pz0r+qW1WX6cuNirCQzog14f2+IfEJIEt+8Gr5NjhhhGbzuEW9ZyYUQQvZuiTAtrlJDhxMIcqhKj+K5/byRBRVqHo6RtNLIirBs1nZT+f/doHHct/obImU+gS1QciQcBg2IGURF0lG0/fEungUObLQKyLLPgpV1UF9cgSAbSh45kwPkXN/dZRESn0mF1W7Gb6qCpFqWfJ4gOW58i+vlB2NZvwLZJyd1xrKIcrg9EEEDDMW7okAw7PgKxlOuqHkAb8Six1lg+NH/HqrASZO0scECjSSBEVUvXbt9Suecy3Jp65uv28aT8DtEGKKqr4pjVQF6wk9J+w6m0ujlSXMDFb31Nw30CYpxMbMyTfPXVZgBKCgsgTEkVL6pW06v3p0iywM7db5JcuYUUYyFwPXZbBbtGjGTqyJFoTrD4AJQ53fipRLS/81q8r+99XL7scl7e8TIP9X/olNYXt8fDu28tRG8PZ9RNHdm8bS/m+kKizr+x3fa/D18ulL8DX6p7Hz5OIq+ghIVffIsjYyN6r52G0CR6n3Muk8aPwOuRObSljIy1RdSVNxIU5893cQLaLkF82isR3fEntYpDUF/0ux+8Ki1OAqKT0UakNad6/zV+zVwuyzLVxVYCwoxodKefGOvfQq7NwbDth5CBEUEmvu6pZNrdU1DLxtwqOjaUUL/kYx64/Ha8gpq+0g5SVyQQ1O8T5obcg03WsKpfWnO00ekgSTJz7lmPrXYtXscOAC597lWikluEVrmtnCP1RxgQOYBNOdU0bC+l50FlWizmhSF4KivJv+QSPCWlxH34H7z9u7OpeBMDowcSpA8Clw3WvQjGUKrSLmXVO6sYbI/Gi5cn+/wHfeUe9hjvIclYwraQ4Vy8/Rf0boFwoYrr+UbpZ2AypUUVROisLFGPI1NKAQQu/vobBAQufljNzkt28tFHH1FZWcmoiZNoSEhmYKA/Uv0a9mfcDECvPkvx/+pmtGVKfpWsr5UcL6LZTNr2lqkxgCv2H6XRK7Gg1+93WJ2fPZ9ntj7DyLiRPNj/QWL8W9fVyTiSzY/zduBfHknkZIlJo4cz67bbETxOHvrgQ/S/wzG7Paqr1xES0n7BUB8t/NH3b59A8eHjFFhtdhZ89xN565ZhspVj1QcRcdYYLp4+lSCzPwVZNexfXURBZjU2nUBZFxPXT06lc9Q/77o9ZLPzeG4xXfwNzEyK/tfkfNhca6XK7WFSWECbfC8Ot5fLXpyPMfEIUWaRqrqzCJGWM6h3PJM734xKEP+rcSg7Ws/On5aRvfFzAG58dy6mkNBTtpclGXeZDU2YAaHJx0mWJJDl36yF5PV6Wf7hIrrnK5a27J5riVj2LTtVsZSMjqNn2FqMugCSOjyHqXIPgjkGOaITSBLy/m/AWorU6woanDLCju3Ytm5GHjmQiIEjEQUBWZaRZbnNFInLVY1KZUSlMijlH7a+izesB5W7tTjKjxBx/V0YunZtbi/JMtcfyGdyeCDnR5x+DZ4T2Vuxly+yvsDmtpGm7UqkJx7RokOqUWOwBOLS2Ok4NIBe6el8PfcLNOVHSD33Unp0Tf3tnf8GsuwlJGTEv+b/4s/CJ1B8+PiLkSSJNeu3s3HRDxhLMvGIGoTU/kyefhFdOiVRV97IiuV5FG+vQPTK1MUb8OscSFB6EBcmhmFW/zHWC5vHS43HqySG+x/4vryWHJsDvShi83rZ2dDI5jorMToNJU43I4NNPJsSS6JR12q7LKud546WYlareCQxitiT+lFgdyIBHQytt/u9WDxe9KKI5iTr0eFGB0sq6rgiOpRQ7R8zO+31erHUVmM2+eNV69GcbkHJ06CurBRjQABag/G3G/+PeG1uRJ0KQS02JzCTJCd2ewF+fil/+431gKWRMTtz+KZHUrv+RKfC65WoLbVRWWChssBKeUE9lYUNyG7lfBwGC1KojdguQZw7bjgNDXb+8/ws/MqyCJtwNVdddeGfdUo+2sEnUHz4+BvJPVrID199iytzMzqvA2t4Kv0mTOGccUNpaPSwYGUe5XuqMJc7kQUoD9MQ1iWYXt3CiIv0x6KW2Vpv4+eqBlySRJhWw/OpMUT9hgn6oNXODQfyKXS4eDolhiuiQ9pk5HRLMhtrLfQ0GwnStL2Be2WZV/LKeP1YOZFaDW5ZRicKdPY3MDkskCnhgWyqs3LvoULKXG56moyk+emRkMmyOjhgtROr19DolahzexkQ6EeqUY8MZFkd7GhQ0tanGHUkGXV4ZBCBQYH+pPjpkWSZbJuDeo8XAaj3eHFIMuFaNWa1Cqcks9/SyOqaBgLVakaFmEjQ67BLEnsbGtlSb8UrK9FTN8SGMT7UTJxei80rsbuhkWybg4sig4j/HwXSqah3e/ioqAqNKHBNTCimP0h4/n/gjqxjrKuxsHNglzbC8zgel5fqYhuVhZYmQWKhusSK5JFBgMBwI2HxJkLj/AmLNxEWZ0Lvp/i55B4t5OfFy2jYsQJBkki+4DouvnDCX3mKPvAJFB8+zgistkbmf7OIgg2/YGqsxGIIIXrg2Uw5/xwiwkNobHCxZ3cZm7aVoDvWiLop/YNLBVajiMqsBZOaPJVEtV6gT0wAfWICiA42YAfsSLhEKPN4WFdvZVlVPcl+BnqbjXxdVkOqUc+U8MDmG/8hm4Pvy2s55nARolFzTUwoo0JMxOi0NHi8bK+38UlxFZlWOw91jOLODqeuS+LwSiyprGNdjYU8uxMBSDLqGRlsYkJYAG5JZkF5LetqLeQ1OhEESDbqGRZkwk8lsqnOSpHDhUYQsHol9jTYsDcljfFTiYRq1EiAWS2iE0WqXB4aPF7UgkAnPz1Dgvyp93jZVGul1OXGIIokG3WcExbAyGAzswsqmF9WQ+NJSb/UAkRoNXzVI4lUv1NXrf69eGWZ9woqePNYOR4ZpCaHyf4BfkTqNHgkmetjw+jTTtbhk7F7JTSCgPq/cFq2eyUaPF4idJrfbnwGsbSyjmsP5PNiaixXxShTXS67h6oiqyJEmgRJbVljs99VcJQfYfH+TYLERGisP1q9IrorqurYvecARw5lU1OQh7eyEJO9Gi8irg69uGzGTXSMj/47T/n/LT6B4sPHGYQkSfyyajNbf/wBv7JsAKzBHYju2Z9x54yhQ3wUNoeb3bk11FY7ECxuTHYJZ4MLW50TS60TS50Twfvb/4aiWkBUicgiOACHIOMRwCsqUTpGrYoQvYYayUu5x4tbAElU1ntFgXC9hi4BRsINWlQqAVElIKpFRJWAStX03vRdeYmomo55fNnx9S3tjn8/4bNKbOqr8tkLlLncCECUTtPGF+S/weGV2GtppNzlxiiKpPrpUQsCl+4/SpHDxS1x4VwSFdw8DVXn9vBjZT3vFVQwPNjEY0nRGH9jOifH5mB5VT1fllaTb3dxXWwot8dH4JVlFlfUsb3eRpXbQ43bQ4HdxfWxYdwaH97u9JMsy3xUVMVTR4rxNP2pU416LowMYniwiUithjqPl+31ViweiUSDDkFQ+rCuxsIxh4sSpwuvDEkGHSNDTNydEEnI75zqkmWZBo8Xs1r1q9M+bklmU50Fr2K84KjdycZaCx0MOoYEmUg06PBXia0j105CkmU+Ka5iVmYRkyQdV4hGKgsVUVJfYQdApRYJifUn7LhVJN5EcLRfc76h/IJS9u7NJD8nh/rCPISaEvxc9QB4BDWN/uFoI+Lo0KUbZ48dQXho4O8aDx9/LD6B4sPHGUp+QSnLl62kZO92/GvyEZCxmGMJTOlKWEwMkdGRxMVFkxAX1SqqQJZlGq0uDpVaqap3oAd0soAogZ8goJLA65GQvDKSV8brlZA8yrvd5UWWZFQSyMfXeWXcbok6lxunW0KUwIiAKMkt+2jen9Tqu/f4Ms8f97MgCLQRQsfFlqqVEDpR8JwkgNoRU837UAuIoiKK3AKsqG1gQ4MNpyBjUKtAJVArefGKAjFGLcVuNwE6NZMig+geYMSgUaaXHJKMQ5LItztZXWPhoM2OXhQYHmzmsqgQOpsMzefTdGYAuGSJjwor+by0GgmBqRGBjAwxE63T4JAlDljsfFtWS4bVzvTIYNL99Xhkme31NtZWW7HLLZYgEdCIAs4mi5NBFOgf6E+yUUesXotRFNljaWRJRR0Ao0PMDAs2kWDQ4vBKWJsqbvupVAQJIjglatQyG2ssrKhqoN7tod7tJVarIc2oRwMU2l04vRKpRj0BKhV1Lg/7LY3UubwIMgiyjBpIN+opdbqpdylTdIIsk+6np5vRQIBahd0jYRAFBI9MZUUjlYUWzFVuAhuV89PoVK2mZ8LiTQRGGlGpRCRJIisnj4z9WRTl5mIpyUdTp6SpB3CKOpwBkRgi44lOSqZrt8507ZyMVvvPsib92/EJFB8+/gGUV1Tz87I15O3agrYyH53kbF4nAw61H26DGdEvEG1gMKbgUILCwjAYjWi0GjQaDRqNWnnXqtFptGi0GrRaDTqtVnnXadBpNeh0WjTqPzalkSzLyNJxwSIjeU4WMk0Cx6MImhOFzYnrTyWEji/zntS+1XGkE7+feJwTlp+8nVfpt4+/F4dOwBOuJzUxkK4pwYTFmQgIMyCIAi6Xm70HssnKOETp0cPYywrQN5ShbSrpYFf74QmKxhSTQFxyCt16dCEtOf4PSLbm48/GJ1B8+PgHUl1bz7FjJRQVl1JZWk5dVSX22mrcllpEWz06ZwMa2f1f719CQBJEJEFEFlTN77KoanoXkUUVnPASVCoQ1Qgq5bMgqhFUalRqFYJKjdj0WVRrEFUq1Go1Ko0GlVqFSq1BrVaj1ijvGq0ajUaLWq1Gq9U0vWvRapre9Rq0akVMabVqdFotOp3mDxdWoITtthIvpxBKTo+XAqsLj1dCKwhoBQGdSsCkUiE2WUfk4wm6Wr+1ytt18k+oJMuUON3UujzoBIEInRaz5jccan/Hz/DJTes9XmrcHrSiiEEUkFH8Vaqr7diKbUR3DSHeT4dGJSIIynSgICjlCgTx+LsAAoiioEz9nLxeaGnXso+W9V5ZRqMW8cgyqAT8DYplw2prZOeeTHIys6nIP4q7ogCjrRK1rJQ6sOkCkUNiCIztQIe0VHr17Ep87Kn9o3yc2fzR929fJlkfPv4CQoICCAkKoHfPtkXvQPFlqa23YLU14nS5cbvcuFxunE43bo/y3e324Ha78TS9u91uvB7ls+Tx4vG48brdeL1evB4PksejvHs9yF4PXo8XWVI+y14J2esByYPkcYHXA5IEkgeP5EWQJATZiyB5lXdZQpS9iLKESvb+YYm/TxRWkqBCPi6wRBUcF1iiCKJaKcaoUiM0Cyw1gkpU3sWThZUaUa28H3+p1RpEtQqNRoNK3WSdahJcJ/tjCCed4ckJ89qc/wnbH9+XBpCAskaBsvabNn0XT/p+8nrhV78jKL11N72Orxe89dS7CghrTCPPoUyjSJKkiDRZ+axYybzIsmKxkiUJryQhS0rNnOZtJAma2kheCVmWmqxsLfs5vszrdmOpKEWqKsbPXo2IjISAxxiKOjQGU6/BJKen0rtXF0KDA08eSR8+mvEJFB8+zgBEUWwWMf8E3B4PTqcbp8uF09kkplwu3C4PLpcLl1t5V4RUk7DyuHG7PHjcbjweDx63B69XEVxetwevVxFVkve4sPIieRRBJXm9irCSlHe8HiS3EyQvSIqQ8jS9K6JKEViiLCFKXkTZi4rfV0n338KBDX/cvmRARlBegvLOCZ9lQUAWRLyGQLQxSQR1GEta10706p6Ov9/pZ+T14QN8AsWHDx//BRq1Go1a/Y+66UiShMfjxeV243C6cLk8OF0uXK7WU2snT9m0N/vStk1r8XPS15apohP60mafJ39v40sj/8b61sepr7dQWlxKSqdktFotKlFEFAVUKpXiXCyKiKKISlQpTsiiCpVK+aysU6FWN7U5ob0PH38VPoHiw4eP/xeIoohWK6LVavD3+/Ozu/rw4eN/wyeHffjw4cOHDx9nHD6B4sOHDx8+fPg44/AJFB8+fPjw4cPHGYdPoPjw4cOHDx8+zjh8AsWHDx8+fPjwccbhEyg+fPjw4cOHjzMOn0Dx4cOHDx8+fJxx+ASKDx8+fPjw4eOMwydQfPjw4cOHDx9nHD6B4sOHDx8+fPg44/AJFB8+fPjw4cPHGYdPoPjw4cOHDx8+zjh8AsWHDx8+fPjwccZxxlUzPl7GvKGh4W/uiQ8fPnz48OHjdDl+3z5+H/9fOeMEisViASAuLu5v7okPHz58+PDh4/disVgICAj4n/cjyH+U1PmDkCSJkpISTCYTgiD8T/tqaGggLi6OwsJCzGbzH9TDfya+sWiNbzxa8I1FC76xaI1vPFrwjUVr2hsPWZaxWCxER0cjiv+7B8kZZ0ERRZHY2Ng/dJ9ms9l3QTXhG4vW+MajBd9YtOAbi9b4xqMF31i05uTx+CMsJ8fxOcn68OHDhw8fPs44fALFhw8fPnz48HHG8a8WKDqdjpkzZ6LT6f7urvzt+MaiNb7xaME3Fi34xqI1vvFowTcWrfkrxuOMc5L14cOHDx8+fPj4V1tQfPjw4cOHDx//THwCxYcPHz58+PBxxuETKD58+PDhw4ePMw6fQPHhw4cPHz58nHH8awXK7t27OfvsswkMDCQkJIQbb7wRq9XavL66uprx48cTHR2NTqcjLi6O22677V9ZA+i3xmLfvn1Mnz6duLg4DAYD6enpvPnmm39jj/88fmssAO644w769OmDTqejZ8+ef09H/yJOZzwKCgqYOHEiRqOR8PBw7r//fjwez9/U4z+PnJwcpkyZQmhoKGazmSFDhrBmzZpWbVatWsWgQYMwmUxERkby4IMP/ivHAk5vPHbs2MHo0aMJDAwkKCiIcePGsW/fvr+px38evzUWc+fORRCEdl8VFRV/Y8//eE7nugBlTLp3745eryc8PJwZM2b87mP9KwVKSUkJY8aMITk5mW3btvHzzz+TmZnJ1Vdf3dxGFEWmTJnC4sWLycnJYe7cuaxcuZKbb7757+v4n8DpjMWuXbsIDw/n888/JzMzk0cffZSHH36Y2bNn/30d/xM4nbE4zrXXXsu0adP++k7+hZzOeHi9XiZOnIjL5WLz5s18+umnzJ07lyeeeOLv6/ifxKRJk/B4PKxevZpdu3bRo0cPJk2aRFlZGaAI+QkTJjB+/Hj27NnDN998w+LFi3nooYf+5p7/OfzWeFitVsaPH098fDzbtm1j48aNmEwmxo0bh9vt/pt7/8fyW2Mxbdo0SktLW73GjRvH8OHDCQ8P/5t7/8fyW2MB8Nprr/Hoo4/y0EMPkZmZycqVKxk3btzvP5j8L+SDDz6Qw8PDZa/X27xs//79MiDn5uaecrs333xTjo2N/Su6+Jfx347FrbfeKo8cOfKv6OJfxu8di5kzZ8o9evT4C3v413I647F06VJZFEW5rKysuc17770nm81m2el0/uV9/rOorKyUAXn9+vXNyxoaGmRAXrFihSzLsvzwww/Lffv2bbXd4sWLZb1eLzc0NPyl/f2zOZ3x2LFjhwzIBQUFzW1O57fln8bpjMXJVFRUyBqNRp43b95f1c2/hNMZi5qaGtlgMMgrV678n4/3r7SgOJ1OtFptq2JFBoMBgI0bN7a7TUlJCQsXLmT48OF/SR//Kv6bsQCor68nODj4T+/fX8l/Oxb/Vk5nPLZs2UK3bt2IiIhobjNu3DgaGhrIzMz8azv8JxISEkJaWhrz5s3DZrPh8Xj44IMPCA8Pp0+fPoAyXnq9vtV2BoMBh8PBrl27/o5u/2mcznikpaUREhLCnDlzcLlc2O125syZQ3p6Oh06dPh7T+AP5HTG4mTmzZuH0Wjkwgsv/It7++dyOmOxYsUKJEmiuLiY9PR0YmNjufjiiyksLPz9B/yfJc4ZyIEDB2S1Wi2/9NJLstPplGtqauQLLrhABuTnn3++VdtLLrlENhgMMiBPnjxZttvtf1Ov/xx+z1gcZ9OmTbJarZaXL1/+F/f2z+X3jsW/3YJyOuNxww03yGPHjm21nc1mkwF56dKlf0e3/zQKCwvlPn36yIIgyCqVSo6KipJ3797dvH758uWyKIryl19+KXs8HrmoqEgeOnSoDMhffvnl39jzP4ffGg9ZluWMjAw5KSlJFkVRFkVRTktLk/Pz8/+mHv95nM5YnEh6erp8yy23/IU9/Ov4rbF44YUXZI1GI6elpck///yzvGXLFnn06NFyWlra77a6/qMsKA899NApHZGOvw4dOkSXLl349NNPefXVVzEajURGRtKxY0ciIiLalIB+/fXX2b17N4sWLeLIkSPcc889f9PZ/T7+jLEAOHDgAFOmTGHmzJmMHTv2bziz38+fNRb/VHzj0cLpjoUsy8yYMYPw8HA2bNjA9u3bOe+885g8eTKlpaUAjB07lpdffpmbb74ZnU5HamoqEyZMAPjHjNcfOR52u53rrruOwYMHs3XrVjZt2kTXrl2ZOHEidrv9bz7T3+aPHIsT2bJlC1lZWVx33XV/w1n9d/yRYyFJEm63m7feeotx48Zx1lln8dVXX5H7f+3dXyh7fRwH8M/08JvEbCwRKUUZKUqsJVFWSolSEil/kmtKXCCRcicu/bkkNyRKlBukiC0bWi6mlOV/Si6Q93PxZD3i+Zmec+zwe79qF9pZ3ufdt/Vp53u2o6N3N9P+zrf6qvuLiwu5urr67TEpKSkSFhbm//vs7EwiIiJEp9NJVFSUTE9PS1VV1buvXV9fl4KCAjk9PZX4+HhFsytNjS4ODg6kqKhImpqaZGBgQLXsSlNrXfT29src3Jw4nU41YqtGyT66u7tlfn7+VQder1dSUlJkd3dXsrOz1ToNRQTaxdramtjtdrm5uXn10/GpqanS2Nj4aiMsAPH5fGI0GuX4+FgsFotsbW1Jbm6uauehFCX7GB8fl66uLvH5fP4B7eHhQYxGo4yPj0t1dbWq5/J/qbE2REQaGxtld3dXHA6HKrnVoGQXk5OT0tDQICcnJ5KYmOg/Ji4uTvr7+6W5uTngXH99/lSCx2w2i9ls/tRrXq6dT0xMiF6vl5KSkv889vn5WUT+udasdUp3sb+/L8XFxVJfX/+thhMR9dfFd6NkH1arVQYGBuT8/Nx/N8LKyopERUWJxWJRNrgKAu3i/v5eRN5+EhISEuJ/X3ih0+kkISFBRESmpqYkKSlJcnJyFEqsLiX7uL+/l5CQENHpdK+e1+l0bzrTIjXWxt3dnczMzMjg4KByQb+Akl3YbDYREfF4PP4B5fr6Wi4vLyU5OflzwRS7MKUxIyMj2NnZgcfjwejoKMLDwzE8POx/fnFxERMTE3C5XPB6vVhYWEB6ejpsNlsQU6vjoy5cLhfMZjNqa2vh8/n8j/Pz8yCmVsdHXQDA0dERHA4HWlpakJaWBofDAYfD8aPuWnnxUR9PT0/IzMyE3W6H0+nE0tISzGYzOjs7g5haeRcXF4iJiUFlZSWcTic8Hg/a29sRGhoKp9PpP25oaAh7e3twu93o6+tDaGgoZmdngxdcJYH0cXh4iF+/fqG1tRUHBwdwu92ora2FwWDA6elpkM9AOYGuDQAYGxuDXq/Hzc1NcMKqLNAuysvLkZGRgY2NDbhcLpSVlcFiseDh4eFT/+/HDih1dXUwmUwICwtDVlbWm9u9VldXYbVaYTAYoNfrkZqaio6Ojh+5sD7qoqenByLy5pGcnBycwCr6qAsAKCwsfLcPr9f79YFVFkgfx8fHKC0tRXh4OGJjY9HW1obHx8cgpFXX9vY27HY7TCYTIiMjkZ+f/2YjcFFRkf89Iy8v78dtFP63QPpYXl6GzWaDwWCA0WhEcXExNjc3g5RYPYF0AQBWqxU1NTVBSPh1Auni9vYWDQ0NiI6OhslkQkVFxavb0QP1rfagEBER0Z/he2w9JyIioj8KBxQiIiLSHA4oREREpDkcUIiIiEhzOKAQERGR5nBAISIiIs3hgEJERESawwGFiIiINIcDChEREWkOBxQiIiLSHA4oREREpDkcUIiIiEhz/gajDYgGGWge/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs and v not in coreVTDs:  #new - skip VRA core\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList + coreVTDs:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)  #we won't draw polygonal HDs for the VRA core nor envelope, but the envelope can be drawn in\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "fde5ee67-7d20-4827-a1d0-5ea365f5587d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency.  this excludes VRA cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "9d4d3d0a-e47e-45ab-b9b2-109ceceaf232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 7059 . Time = 0\n",
      "working on tract-tract distances for unit 400 out of 7059 . Time = 214\n",
      "working on tract-tract distances for unit 817 out of 7059 . Time = 414\n",
      "working on tract-tract distances for unit 1217 out of 7059 . Time = 598\n",
      "working on tract-tract distances for unit 1617 out of 7059 . Time = 755\n",
      "working on tract-tract distances for unit 2017 out of 7059 . Time = 911\n",
      "working on tract-tract distances for unit 2417 out of 7059 . Time = 1057\n",
      "working on tract-tract distances for unit 2817 out of 7059 . Time = 1184\n",
      "working on tract-tract distances for unit 3217 out of 7059 . Time = 1286\n",
      "working on tract-tract distances for unit 3618 out of 7059 . Time = 1376\n",
      "working on tract-tract distances for unit 4019 out of 7059 . Time = 1469\n",
      "working on tract-tract distances for unit 4419 out of 7059 . Time = 1555\n",
      "working on tract-tract distances for unit 4819 out of 7059 . Time = 1624\n",
      "working on tract-tract distances for unit 5292 out of 7059 . Time = 1678\n",
      "working on tract-tract distances for unit 5868 out of 7059 . Time = 1719\n",
      "working on tract-tract distances for unit 6346 out of 7059 . Time = 1748\n",
      "working on tract-tract distances for unit 6791 out of 7059 . Time = 1764\n",
      "All done; total elapsed  1767 seconds for WI with 8 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 5.000443356135309\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"uu\" nomenclature\n",
    "#   note that VRA core vtds are assigned a large distance from every vtd\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "b7a1da66-4161-46d4-b2e6-5777a5c450ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 7059 . Time = 0\n",
      "working on unit-unit angles for unit 400 out of 7059 . Time = 111\n",
      "working on unit-unit angles for unit 817 out of 7059 . Time = 216\n",
      "working on unit-unit angles for unit 1217 out of 7059 . Time = 314\n",
      "working on unit-unit angles for unit 1617 out of 7059 . Time = 402\n",
      "working on unit-unit angles for unit 2017 out of 7059 . Time = 484\n",
      "working on unit-unit angles for unit 2417 out of 7059 . Time = 563\n",
      "working on unit-unit angles for unit 2817 out of 7059 . Time = 629\n",
      "working on unit-unit angles for unit 3217 out of 7059 . Time = 690\n",
      "working on unit-unit angles for unit 3618 out of 7059 . Time = 743\n",
      "working on unit-unit angles for unit 4019 out of 7059 . Time = 790\n",
      "working on unit-unit angles for unit 4419 out of 7059 . Time = 835\n",
      "working on unit-unit angles for unit 4819 out of 7059 . Time = 868\n",
      "working on unit-unit angles for unit 5292 out of 7059 . Time = 897\n",
      "working on unit-unit angles for unit 5868 out of 7059 . Time = 919\n",
      "working on unit-unit angles for unit 6346 out of 7059 . Time = 934\n",
      "working on unit-unit angles for unit 6791 out of 7059 . Time = 942\n",
      "All done with angles; total elapsed seconds = 944\n"
     ]
    }
   ],
   "source": [
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "#plt.hist(uuAngle)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "09552570-4c54-4478-943f-53daa43f64a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "736714.75 8 5893718.0 8.0 5893718.0\n",
      "5893718.0 5219563 0.88561  + VRA 0.11439\n",
      "statePop, summed non(VRA+skipped) pop, summed nonVRA frac pop, VRA frac pop\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), r5(np.sum(HDweight)),\" + VRA\",r5(corePop/statePop) )\n",
    "print(\"statePop, summed non(VRA+skipped) pop, summed nonVRA frac pop, VRA frac pop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "4ceeb6d4-f81b-479e-baa0-1421ed5a0cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "e25e2932-4933-4435-9adc-b178ec47e5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 707.105 of targetWedgePop\n",
      "I am working on HD number 0 of 7059 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 7059 HDs.  Elapsed sec = 132\n",
      "I am working on HD number 818 of 7059 HDs.  Elapsed sec = 239\n",
      "I am working on HD number 1218 of 7059 HDs.  Elapsed sec = 356\n",
      "I am working on HD number 1618 of 7059 HDs.  Elapsed sec = 457\n",
      "I am working on HD number 2018 of 7059 HDs.  Elapsed sec = 576\n",
      "I am working on HD number 2418 of 7059 HDs.  Elapsed sec = 707\n",
      "I am working on HD number 2818 of 7059 HDs.  Elapsed sec = 822\n",
      "I am working on HD number 3218 of 7059 HDs.  Elapsed sec = 943\n",
      "I am working on HD number 3619 of 7059 HDs.  Elapsed sec = 1086\n",
      "I am working on HD number 4020 of 7059 HDs.  Elapsed sec = 1221\n",
      "I am working on HD number 4420 of 7059 HDs.  Elapsed sec = 1340\n",
      "I am working on HD number 4820 of 7059 HDs.  Elapsed sec = 1476\n",
      "I am working on HD number 5313 of 7059 HDs.  Elapsed sec = 1580\n",
      "I am working on HD number 5892 of 7059 HDs.  Elapsed sec = 1708\n",
      "I am working on HD number 6427 of 7059 HDs.  Elapsed sec = 1825\n",
      "I am working on HD number 6827 of 7059 HDs.  Elapsed sec = 1929\n",
      "2004.69 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.98759 0.13606\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZcB75p646XI3krRs0V0k1Fiwr6/izJSKSjlNjPiB3ykK8QTd8jcGDaQ5/XCXlD/UswbCyzh9/tkRE/oqBkMrcbdqn9A3a31ZJ+VM9S6CvrPO3ny0RkT/j1JiKPJ2yEG/QI9NbI7NDC4+yFP60Sor1LMryp58tEZG/YyCkIjlTFt7mzSJsOVjPojx/+dkSEQUCBkIq8qcpC39ZJeUvwaHU4nWp43zJX362RESBgDVCKvK3KQtvFGHL5Q/BodT6JH+qY3LFH362RESBgIGQisQpC0cZEF807fP1KilfB4dSi9cDcWd6X/9siYgCAQMhFYVoNXioVxI+2F7scIwvpix8uUrqWkUNtBrA0QyTN4NDqRvaDu6aqNjGt2oL9BVwRETexhohFeUWluBDJ0HQs4NS/S6r4E25hSWYvPKAwyBI5K3gUGp90v/LP+MXdUxERKQ8BkIqcZZ9EK07VOKXxbfeIOV6aDXAwrG9vRYcSq07Olt6U9HjERGR/2AgpBJX2QegaWUVpFwPswDEReu8dg5S647ax0cpejwiIvIfDIRUYiyXli2QOi7QNFx2LvVzejPLIrXfzu8yU9iXh4goSLFYWiWlN6oVHecN3trry96y8/joMEmv9WaWRey3M2nFAWgAm2k663474aFaSeP8rVCaiIhcYyCkkvjocEXHKc1bPXIcLTsvrah1+jq1WglI7bcTjH15/GGTWyIiX2MgpBKDPlLRcUryVo8cKQXR9qidZZHabyeY+vIEUnNIIiJvYiCkElfNFAHpdSZS/pKX+te+1F46DXvk1JkF7Dp9FfmnrgIQkHlHS2Q02AhWSkG0Pb7IskjttxMMfXkCsTkkEZG3MBBSiZRmig/1SnKZXZDyl7ycv/bl7PUlBgC5hSWY8cURlN28Pb214JtTiI0Kw7yHe1jew91C55k53Xgj9hJ3A18iomDFVWMqqTMLWHeoxOkYV32ExL/kGwYu4l/yuYUlksZYk7vXV25hCSauOGATBInKbtZiotV7uFPorAEwZ+PxJtNPSW3+ssktEZG/YCCkEk/7CLn6Sx6o/0t+9rqjLsdYBxly9vqqMwuYve6Yy7Gz1x1FnVlwuTzdnmC/EUvZvd6bO9z7wya3RET+hFNjKvH0BiT1L3ln7E1zicGK0VRlN4CyXr21p7hUUv8fY3m15T0cLTt3JRhvxEpPa7rD15vcEhH5G2aEVOLpDUjJwMD6WGIvHQCNMjcNV2/JOQdx7LC0JCwc2xtxEvsGiYLtRuyNaU05xCyTsbzKaQ8nNockoqaGGSGVyMm82KNkYNDwWFJ75Mg5B3FsbmEJ5mw87rJvkLXYyLCguhFLKVCeve4oAI1XipjtZZnsYXNIImqKGAipRGoXY0c3IKmBlCAIuFhe7XRM3/ZxyD911WZpfcMeOS2b6QABuFJRjfxTV9EvNR79UuNhiIlwOT1miNGhX2q8w2Xarjw1IMUvbsRKNRyUMq1pLHfeUdzetKYUcn4GgdwckojIXQyEVORJd2KpgRQATFxxwO4xBNQv0b/vnW8c1qBkdmiB3MIS/PGzQzZjYiPD8NSAFLzx6+74w0r7xxfNfuhOAHCrmWJcVBimDO4k81XKU7JWx1vTmq64amipQX0n89dzusGgjwzY5pBERJ5gIKQyT7oTSwmkXNWR2OtjZN1ID4DdDEJZZS3mby5CbFQYnhuUik/2/dxoCb11H6H8U1dlN1PUAJj7cA+f34yVbjjozWlNZ6Rkoq5W1MCgjwz4JpFERO5iIOQDnnQndhZIiRkAucQbvrM6FVHZzVp8uL0YC8f2hj4q3GFnablZEH/Z3sEbDQelTGsmxugAaHCx3L0aMnu4VJ6IyDVZq8ZSUlKg0WgafU2ePBkA8Nxzz6FDhw6IjIxEq1atMHLkSPzwww82x9iyZQvuueceNG/eHAaDAdOnT8etW7dsxhw+fBj33nsvIiIi0LZtW7z99tuNzuWzzz5D165dERERgR49emDTpk02zwuCgDfeeANJSUmIjIxEVlYWioqK5Hxcn3HVR0YMpEamt0amVfDh7pYWImN5taTl8QLqmx5m3NECf8zugj9md8WATi1tAgOpmYvfpCfjX7/vjx3TB/s8CAK803BQysq82Q/didkPSVu9JxWXyhMRuSYrENq7dy9KSkosX3l5eQCARx99FADQt29fLFu2DMePH8fXX38NQRAwdOhQ1NXVAQAOHTqEESNGYNiwYTh48CA++eQTrFu3DjNmzLC8R3l5OYYOHYr27dtj//79eOeddzB79mx8+OGHljHff/89xowZg6effhoHDx7EqFGjMGrUKBQWFlrGvP3223j//fexZMkS7N69G9HR0cjOzkZVlX/89eso2MktLMHAt7ZizEe78MLqAoz5aBcGvrVV0tJpNf+ydxUMSG2muKbgAv742SHkHTMqe4Ju8lYWRZzWNOhtgw6DPsIy1SZljByufgZcKk9EBGgEQXC7be3UqVOxYcMGFBUVQaNp/Ov28OHD6NWrF06ePIkOHTrgT3/6E/Ly8rB3717LmPXr1+Oxxx7DpUuX0Lx5cyxevBivvfYajEYjwsPDAQAzZszA2rVrLdmlxx9/HBUVFdiwYYPlOBkZGUhPT8eSJUsgCAKSk5Px0ksv4Y9//CMAwGQyITExEcuXL8fo0aMlfb7y8nLo9XqYTCbExMS4e5kacVSI+1CvJHy4vbjR1Ih4ZV3dDPNPXcWYj3Ypdp6uvDc6HSPTWzt8Xqy1AZw3U5T6+dQg9RqueibDrelNJTfMlcLRz8CfrjkRkdLk3L/dbqhYU1ODFStWYMKECXaDoIqKCixbtgypqalo27YtAKC6uhoREbZ/7UZGRqKqqgr79+8HAOTn52PQoEGWIAgAsrOzceLECVy7ds0yJisry+Y42dnZyM/PBwAUFxfDaDTajNHr9ejfv79ljK84appXYqrCB3aCIMDx9hgNubOlRUP6SOmND89cqbD5vmGWa0h3g90MR0PCL1+uPp8avJ1FcTStKXeMVEpnmYiIgo3bgdDatWtRVlaGJ5980ubxRYsWoVmzZmjWrBm++uor5OXlWYKa7OxsfP/991i1ahXq6upw/vx5/PnPfwYAlJTUT/0YjUYkJibaHFP83mg0Oh1j/bz16+yNsae6uhrl5eU2X0pytZzZGSm1Kc5qUaSaMCBV8thVe865nNIDgB3TB2NmTjeXxysxVWHBVt/WccnptB0ohqUlYcf0wVj1TAbeG52OVc9k+E1NFhGRr7kdCC1duhTDhw9HcnKyzePjxo3DwYMHsW3bNnTu3BmPPfaYpS5n6NCheOeddzBx4kTodDp07twZI0aMqD8Rre93+5g7dy70er3lS8xkKcXTYmYA2HnystOsiaMMQJI+ArFRzrM9SfoITBncEUvG90EzXYjLcxH3FHO1NUTeMSNaNte5PB4AzN9c5NFWEkoIxiyKklkmIqJg4tby+bNnz2Lz5s344osvGj0nBhGdOnVCRkYG4uLisGbNGowZMwYA8OKLL2LatGkoKSlBXFwczpw5g1dffRV33HEHAMBgMODixYs2xxS/NxgMTsdYPy8+lpSUZDMmPT3d4ed69dVX8eKLL1q+Ly8vVzQYUqKYecE3p/DvA+edLjV3tMQ+75jRYZdhDW5nOoalJaGy1oxpnxS4PB9jeRXezv3B5XLz/3m0l+TP6O5WEkrypN8TEREFDrfSMMuWLUNCQgJycnKcjhMEAYIgoLradvsAjUaD5ORkREZGYtWqVWjbti369Klv5peZmYnt27ejtvZ2s768vDx06dIFcXFxljFbtmyxOWZeXh4yMzMBAKmpqTAYDDZjysvLsXv3bssYe3Q6HWJiYmy+lKTUMmUpm3DaywCImY4kO9kiMdMh1vn856i0VVylN6olLTeHgEbv64g4BeiqjYC3eSuL4uvPRUREt8nOCJnNZixbtgxPPPEEQkNvv/z06dP45JNPMHToULRq1Qo///wz5s2bh8jISMv0FwC88847GDZsGLRaLb744gvMmzcPn376KUJC6qdixo4dizfffBNPP/00pk+fjsLCQrz33nuYP3++5RgvvPAC7rvvPvzv//4vcnJysHr1auzbt8+yxF6j0WDq1Kn4y1/+gk6dOiE1NRUzZ85EcnIyRo0a5e618pirxnqihltoNGRdPC03czIsLQmDuybi/+WfwdnSm2gfH4XfZaYgPFQreXNOa+eu3ZQ07kpFNWbmuN6eQ5R3zIgXPy1QZIsLf6Lk1h1EROQ52cvn//Of/1hWcXXu3Nny+IULF/D73/8e+/fvx7Vr15CYmIhBgwbhjTfeQJcuXSzjBg8ejAMHDqC6uhq9evXCrFmzMHz4cJv3OHz4MCZPnoy9e/eiZcuWeP755zF9+nSbMZ999hlef/11nDlzBp06dcLbb79tE3AJgoBZs2bhww8/RFlZGQYOHIhFixbZnLMr3lg+72o587ODUrHuUInkYGRaVie8kCX9M8lduu+KRgNI+Re06pkMAPBoeX+gL/l2tHVHoH8uIiJ/I+f+7VEfoWCndh8hMStQZxYwP+9HLPjmpKTjLZF4A3V3N3hPxUaGYv/MoVhXcB7TPj3kcryzjJi41cSO6YMDql6nzixg4FtbHQa4gfq5iIj8kZz7N/ca8wFXhbghWg0GdGwpORCSMkXmydJ9T5VV3sLbuccl10i5mhYUa4gCaaNQOVt3BNLnIiIKdL5fs94ESekcLNYTSSFl7ysllu574oPtxfipVFo9kRSBtlEoN0AlIvJPzAipTGqxrNjYb+IKacXFrjZL9Ycb7BcHzyt2rEDbKJQboBIR+SdmhFTkqvFgw+Xww9KS8Ns+jvfysraz6LLT5/3hBltedQvx0c6bOhpidDDEBN9GodwAlYjIPzEQUomzGh1ne4kN6NhS0vHzjl1EnVmw9KhZc/A8ln53GmsO/Iz8U1fRt32cpH3IvF2m+5v01tDYeR/xsdkP3YnZDwXXFhdAcG7dQUQUDDg1phJ3i2UN+khJxzdV3cKCrSexeu85u+9jvUS+4aosd5fuuyOruwF3p8Y3mh40NJgeXDy+j8sxgUZsaBlsn4uIKJBx+bwTSi6f/7LgPF5YXeBy3Huj0zEy/fZ0WJ1ZQN85eSirrHXyKumesxPsNFy6LxZyF128jgXfnFLkfcX32fby/dh/9hqM5VUovVGN+OhwGPSRdgvGHRWVSyk292eBfv5ERP6Oy+f9kLvFsiFaDZ4akIr5m39U5Dw+2fcz9vwpC/vPXnO4dF/MSOWfuiopEPptn9b4/IDzQmgNgId6JeG+d76xG4TZCwSsz0UUDJ2Z7X0uIiLyDdYIqcSTYtkpgzu63DleqrKbtVj87SlJe2hJPee3ftsLS+zsYSZK0kfg2UGp+HB7saRCcUd7ccktNiciInKFU2NOKN1Z2tX2Gs62WMgtLLG7lN7VvmT2ROtC8JdRPWCIcT0t4+icRYvG9saInskAbk/5GE2VKK2oQXyz+hVgfdvHNcoENfwMYlflvGNGuxmfmTndMGfjcXZmJiIil7jFhkK8tdeYu1M7jl47+u62mL+5yK3zkfLezjZjlfL6/FNXJe0xNi2rE97dXGR3Ly6p/0hXPZPBaScioiaOgZBCvLXXmCfFsvZeCwAD39rqcld7e6Ru+LnpcIndneOdvV48168KS/DP/LMuzyU2MszjovCGxeZERNT0sFjaz3lSLOvotbMe7G6ZwpJDQH0w42y/sjqzgDkbj8l6vbMskiNKrIzzh8aRREQUOFgsHSSGpSVh4dg+cKc8xrqHkT1yeiABwKbDFzDRTlGzIxrA42JwdmZuGhwV0hMRuYsZoSASFx0OT+4LjvYjk7pP2X+OGnGtogZTVh2U/J5i3PbUPdJbBDhqCMnOzMEtGFonEJH/YUYoiHi6saqjaSWp003Lvj+DP6w8ICsYi4sOw4QBKbgrJQ6GGJ3TLT60GuCZe1NhaLBMPzFGh6lZnVB9yxyQWQI5WY6mmhFh6wQi8hZmhHzEG92Fz1ypcOt14tJzR9NK/VLjER8djtKKGg/Ozr7Silos3XkGS3eeQWxUmNNib7MA/P27Yiwc2wdx0eG4dL0KZ67cxKo952xWzQVSlkBOlsPfMyLe6pjtap8+VzVuRETOcNWYE95aNeaNG5qjPkOuSF019uf1R/HxzjNunZuccxEAaDSAo3+VDXsOTVpxwO5ye8D1Z/I1Mcsh5fyd9ZFqONYVbwQs3gzSpLZfYOsEIhLJuX9zakxl7qb4nU2JiH8xS9GwKNmgj5B0E32gW6Kk43tC/ETOQnOxMHvX6atOswRAfZbAX6eOXGU5gNvnX2cWMOOLI3aPI/ez5haWYOBbWzHmo114YXUBxny0CwPf2urR1JK3p62kTvl6OjVMRE0Tp8ZU5G6K39Vf265WdYmmZXXGlMEd3csG+Fk8kX/qquSVbP6YJZCzEm9PcSnKbjpuLSD1szrKQIkBizsZtDqzgNnrvDtt5e4+fUREUjAjpCK5y9ABaX9tS/1LOKVllKUPkat9xhra8sNFSePUIy0y89csgdTzMpoqsWxnscfHlJOBkmPB1iIYy+X9m5bLk336iIhcYSCkorxjRknjxBua1JtXy2Y6Scd19y/m3MISr9cHSSXe9DLvaClpvL9mCaSeV2lFjeRGk86O6U4Q7kpuYYnkrV08CUhDtBrMerA7ADQKhtg6gYg8xUBIJXVmAWsLLkgaK97QpN68IMBrfzHLqT9SgthcUQPnN72MDi0COksgNcsRLzHIjY0Mc/pZla6zkfvvwtOAdFhaEhaP79OodYLUGjciIkdYI6SSPcWlkpafx0ffvqFJvSldqai2bLGhdLNBqfVHShEAPH5XG/RuF9eoLsrQYBWStz6zGsQsh6vz10eGSzreUwNSnH5Wpets5Py7UCogHZaWhCHdDV5Zok9ETRcDIZVIDWp+k97a8otdzs0rs0MLLB7fx2XwIEedWcCOosuyX+epD7YXY8n4OOyYPtjpTU/MEij5mdUk5fzrzAKS9BFON9SNiwrDlMGdnL6XmIFydBxXvaQakjPVpWRA6sk+fURE9jAQUonUoCaru8Hy/+XevJT8i9mdTVMbZjY8Ia40cnXTC/Qsgavzd5Y5Auqv+dyHe7j8vFIzUFKvm9R/z9OyOvt9QEpETRsDIZX0bR8HrQZOt5/QaurHidy5ebnzF3PDBnvXKqoxeeVBWUHNE5nt8J9jlxSbRpOz9D3QswSuzt9R5khuw0IlM2iugnQAMMToMGVwR8nHJCLyBQZCKtl/9prLPbjMArD425N4Iauz5TFvT//Yy/xoNfIzO2YBeG14N7y69giuV93y6JxE/rr03ReUynwpdRzrIN2RkenJAZOZI6Kmi1tsOKHkFhtfFpzHC6sLJI1dYmcVjLe2RbDXYM9fBNqWCeLPyGiqRGlFDeKb6WCI8d1Unbf2/rI2d9MxfLDdfp8jDfx/mxMiCk5y7t/MCKlEzvJhe514lZ7+cdajyNfkFu76A2c1Vb7YGFWNDVrrzALWHXK+fQY3QyUif8c+QioRayqkkNrYztn+Y66ovSxeKmeFu558XiU4en9H3b9FJQrtuSWVt/f+EnmjSSMRkdqYEVKJWFMhdYd4V/Uxnv7F76/1N45qn+x93vjocIxKT8aQ7gavTz85ut4zc7pjzkZpmTU1siPu7mfnDm6GSkTBgIGQioalJWFaVmfM3/yjy7HOptKU2DzzzJWbUk7Z6+KjwzDm7nbQaIDMO1oiw87+Z44+b2lFDT7eeQYf7zzj1eknZ9f7DyulBbZidmT5zmI8OSDVa8GQnCyNp1Ot3AyViIIBp8ZUNmVwRxhinG+bYIjRNaqPEadl1hz4GX9aU+jR5pl1ZgGr9pyTeebeUVpRi4XfnsKCb07hj58farQfm9RaJiWmn+xNfUnZ702OORuPY+BbW702TaZmloaboRJRMGAgpLIQrQazH7rT7l5aoqpbZpuAILewBAPf2ooxH+3CtE8POd2qQ0pdhqsdw12Jjw53eO6esFfDIreWyZ0d1AHba/zC6gKM+WgXBr61FQu2FileS6V0rY41NbM03AyViIIBAyEfGJaWhIVj+yBaZ39m0nSz1nKjdFWI64ijv/jl7BhuT2xUGP4yMg2A40DOXcIvXzP+fQQ7i66gzizIyly4W5zrrLjYk2vliNTMnTvUztJwM1QiCnSsEfKB3MIS/HnDUdyott940LqoVRAEt6Zg7P3Fr8RO8g/3bo246HAsHNsHczbK24JDqrLKWoxbuhtJ+giMvrut7NfLCZ6UnvqSSslaHWtKb6UhRaBvc2JNjd5LRORfGAipTGoTQ/FGKZezHjxKLJm3Lk6emdMNcdE67Ci6jIXfnvLouPaU/JKRiY0Kg+lmreTARM60j6fXRLzeM3O6Yc7G44pl7jzhi81oA32bE0Cd3ktE5H8YCKnI200MXf3FbzRVKvZeJaYqTF55EIvH98E9HVt6JRACbKffXG3q6k4jRk8CEevrPSwtCdlpSZbO0gfOXcP/2+W6IN1bK6qCKUujBiVWYhJRYGIgpCJvNzF09hd/bmEJ5mw8ruj7Caifvnt5aBdFj9vwPcpu1mJaVmes3nvO4fWTOu3TcOqjZbTzFXzONLze1lmRh9JbY/PxSw43JVWje3agZmnUnp5Ss/cSEfkfBkIq8mZjuZk53Rz2p/HmnmIlpirMWn/UC0e2ldIyCjumD8ae4lJsPmbEmoLzKK2otTwvZdrH3tSHISYCsZFhKKusdfg6e6bc3wHThnRxeGMM0WowM6cb/rDyYKPn/GVFlT/Ww/hiekrN3ktE5H8YCKnIm43lzpdV2r2JqbGnmFK7zTuT0DzCkuHI7NACf8rpLusm7igYvFhuP2PjSmaHlk7fb9PhErz+pf0AUR8ZhqcGpGBId4Mb76wMf6yH8dX0FDtkEzVtXD6vIjn7jcn18c4zdvvS+OueYnJdNlXZNDsEgMwOLTAyvTUy7XSjtuaVlWFOXjh30zH8YeUBh/2eyiprMX9zkVcbKzqj1l5kckj5GXmj3QDADtlETR0DIRWFaDV4qJd3/toW6xga3ijk/hWbpI/AtKzOmHJ/RwXPznMvfFbQqNmh1Bu2N4LBKxXVdh/fdPgCPtheLOkYvgg8fBlwOOPLDVzZIZuoaWMgpKLcwhLJN0m5HN0opP4V+4dfdcDMnG54ZVhX9EuNx3890Mlr2St3CA3uy3K21PDGlIajPk2vf1ko+Ri+CDyUDjjsbUviDl9OT7FDNlHTxhohldSZBcz49xGvv0/DG4X4166j1UtA/S/7lXvOoezm7YLhJH0EHuqVhA+3F3u1vsgTAoBXvzjicjWP0lMa8dFhDvs0WRdwS6F2Ia6SAYeSdUa+np7yRe8lIvIPzAipZNepq7JXJrmj4Y3C+q9dR8Ql6taMpip8sL0Yg7u2Qnx0mM1zGj/6w/jazVos2Np4GwzrTIXZLMAQ43rqY8HodEj5o/8vI9MU79OkViGuUgGH0nVG/jA9NSwtCTumD8aqZzLw3uh0rHomAzumD2YQRBTkmBFSSf7pK149vrO+NPV7m/XGlFUHIXXmQhy25YfLAOo3Wu2fGg9dqBZrCy4oc9IK+fC705j0q44ID62P6+1lKmKjwiw9YRpeAgHA6LvbYnjPZCzQavGHlQccvtdzg1Ixomdyo8c97dOkViGulAxhfHQY+raPc3gMT/vuOFq2r/bWIPYEau8lInIfM0IqUXJ6yZ06hrhoneQgyJ7Sihp8VWj0uyAIACqq65Axd4vTTWpNv2S89FFh9g5hWcWl1QJLxvdpVB/VIjoci8b2wasjbLNrdWYB720uwsQVjleJOaN2Ia6zehhRaUUt7nvnG4dZHU/qjHILSzDwra12C9+5gSsR+QIzQiqJjQz36PVaDbBgTB8AwOtfFtrcdOv3uuoOfWQ4viw4b7evTqD2QHG1rYaotKIGk1YcgP6XzE9DYqYiIlSLqQ90wrtbGk+nWferEZs3OutTlFtYgtnrjsJYbn8FmSu+KsR1VA9jzVnvHnfrjKT2CeLWIESkJgZCKol3kImQyiwARZduYPXeczZBUHx0GEb0MOD1L4/YFOo2LFoN1B4o2XcmIvfoRUlj7dU6NXzeWF6Nf+466/B562kdZ1Mkcrt1R4RpoQvVwlR5u/mkLwtxh6UlYXDXRGTM3WI3k+VsisudOiO502mcniIitTAQUokShdLzN//Y6LHSilos3XGm0ePi8vKFY3sjLlqHC2WVaKYLwY3qOo/Pw11hIRr8qnMrJDTT4V97f5L0mt9lpmBXcanTAEcuZ1NYUlZxudOte+7DPfFQr2S/ynTsP3vNrWvhqs7IXr0at7EgIn/FQEglcVGeTY25QwBkFUh7W22dgLzjlySPj48OR8YdLTDv4R6YuMJxAbM3OJv+cadBoyEmwu8yHe5OcblT2MxtLIjIX7FYWiXXbsovpFWCvwRB7hiVnowQrQbD0pKwZHyfRsv45dIAiNaFSBrrqGFi/qmr+Erm0nBfdyV21PTQk6X0cgubfd0niIjIEWaEVBIfrX5GKNC1jo1EnVmwBEPOalqA+kAnNioM127W2s1UCKhfYeZKVHgIzGbB8t6A/SX5Uj3UK8lnU2DOmh4O6W6QPcVlTU5hszvTaUREatAIQsPNC0hUXl4OvV4Pk8mEmJgYj46Vf+oqxny0S6Ezazrio8Pwl5Fplt49YpEyYH9KZvH4+pV1DW/++shQ3DILkgIhkRgwAJBVGN2Q5pfz8mZRtL3ePHnHjE7P++kBKYiJDMP8zY1X0FlfT6XOW8rPjkvkiUgJcu7fDIScUDIQqjMLGPjW1qDYCd4XnhuUaunhI2VrhzqzgAVbT2LZzmK3C9XFLFJsVJhHxdpitmPH9MFeyQzZux6GGB2qbpklnbdG03gvt7ioMMx9uIfigYmS23IQETki5/7NqTGViDvPe2vT1WD3wfZi9GoThxE9kyRNyeQdM+LdzT961MhSfK2nK9akrkRzZ0WZw948Mnob2ftT6JqCq/SssU8QEfkbBkIqqTML+GTfz74+jYA288tCZKfV95lxtAKrzixg1+mrmPHvI363WayjFVHuZEm8/TldbZPhjKugzt9WzxFR08ZASCW7Tl9VtBdOU3S1osZpVsWTgmY12FsRJbXbskiJKT8p3O3rw6kvIgo0XD6vku9PeXfTVXsiwoLvx+ssq2JvjzFvE/cKM8ToZO+c7qrbMlCflRGXu+cWlqDvX/Iwf/OPXg2CrMnp66P0jvRERGqQdadMSUmBRqNp9DV58mQAwHPPPYcOHTogMjISrVq1wsiRI/HDDz/YHGPv3r144IEHEBsbi7i4OGRnZ+PQoUM2Yw4fPox7770XERERaNu2Ld5+++1G5/LZZ5+ha9euiIiIQI8ePbBp0yab5wVBwBtvvIGkpCRERkYiKysLRUWNV8eo5fy1SkWOExUWghce6AhDTITDGy9QXwBbVWtW5D39iaP+PnI7PUslLsnXwPlmt7MfutPmMXtjGk4xyem2nFtYgokrDqieVZTa10duUNfwtfb6HBERqUFWILR3716UlJRYvvLy8gAAjz76KACgb9++WLZsGY4fP46vv/4agiBg6NChqKurX7J848YNDBs2DO3atcPu3buxY8cONG/eHNnZ2aitrf8FX15ejqFDh6J9+/bYv38/3nnnHcyePRsffvih5Ty+//57jBkzBk8//TQOHjyIUaNGYdSoUSgsLLSMefvtt/H+++9jyZIl2L17N6Kjo5GdnY2qKt9Mmxj0OkWOc7O2Du9tOYm7UuKc3viDcS2go8aEcjs9a1C/m/z//rYnZuZ0w5OZ7R2OA4B5D/dw2TzQnZ3TpWZbjOVVeHP9MUljG55/bFQYDDHymxQ6ymI54u6O9M52oyciUoNHy+enTp2KDRs2oKioCBpN4/zE4cOH0atXL5w8eRIdOnTAvn37cPfdd+PcuXNo27YtAODIkSPo2bMnioqK0LFjRyxevBivvfYajEYjwsPrmxDOmDEDa9eutWSXHn/8cVRUVGDDhg2W98rIyEB6ejqWLFkCQRCQnJyMl156CX/84x8BACaTCYmJiVi+fDlGjx4t6fMpuXz+o+2n8d+bjnt0jKbMWS+eLwvO44XVBZKPA6tjOasrsrck39VqJzmrv6T2lpqZ0w1zNsr7t2P9OcVVWnnHjPh455lGzSadvVZqXY/Un8F7o9MxMr01AMf1UewrRESeknP/druIpKamBitWrMCECRPsBkEVFRVYtmwZUlNTLUFPly5d0KJFCyxduhQ1NTWorKzE0qVL0a1bN6SkpAAA8vPzMWjQIEsQBADZ2dk4ceIErl27ZhmTlZVl837Z2dnIz88HABQXF8NoNNqM0ev16N+/v2WMPdXV1SgvL7f5UkrL5spkhJqiJCdZFUDetgxx0WGYMCAF+shwbDp8wWld0cwc2wJfcbXTyPTWyOzQwm6AE6LVoF9qPBKaR+DS9foMiKOpHrHbsqvaIne6kltnosTzfuPBO7HETtaq4cdwlsVyRO4WGp5MpRERKcntVWNr165FWVkZnnzySZvHFy1ahFdeeQUVFRXo0qUL8vLyLEFN8+bN8e2332LUqFGYM2cOAKBTp074+uuvERpafypGoxGpqak2x0xMTLQ8FxcXB6PRaHnMeozRaLSMs36dvTH2zJ07F2+++aacyyBZgsRAaOoDnbD8+zOqFcP6u9dGdMOEgalOl3D3S42HISYCxnLHUzPR4SEID9WitKIWS3eewdKdZ6DVOM6MaADM2XjMslxfKjmrpqRuXqqPlB4I6SNDsWhsX2Q4CNTs9fHp2z4O+89e86ivj9wtNLgbPRH5C7czQkuXLsXw4cORnJxs8/i4ceNw8OBBbNu2DZ07d8Zjjz1mqcuprKzE008/jQEDBmDXrl3YuXMn0tLSkJOTg8pKZYqJPfHqq6/CZDJZvn766SfFjm2W+Jdt3/ZxWDi2j2LvG+hMlbUub8p5x4yoqrW/dYb4yoqaukZNAp39SBzVtNgjFvv+ef1RTLSTYSoxVWHiigOYs/5oo2LgYWlJeHZQKhomVTUa4NlBqRiWloR+qfGSN5ydMCAVAzq1dHrNGma2wkO1LjNdrohBHSCtYJy70RORv3ArI3T27Fls3rwZX3zxRaPn9Ho99Ho9OnXqhIyMDMTFxWHNmjUYM2YMVq5ciTNnziA/Px9abX0MtnLlSsTFxeHLL7/E6NGjYTAYcPHiRZtjit8bDAbL/9obY/28+FhSUpLNmPT0dIefS6fTQafzzhTWbgk3VAD494GfcV+XBMRHh6G0glkh59UssKymciQ2KgwC3O8O7epGLKd3kZiJio8Ox6j0ZAzpbsC1ihp8uL240ac0C8CH24vRu10chqUl4TfprbF05xmX71F9y4z/+foHAPXBTsYd7gU27hALxhtt92EnI8bd6InIX7gVCC1btgwJCQnIyclxOk4QBAiCgOrq+nb/N2/ehFartakpEr83m+uXemdmZuK1115DbW0twsLq/wrOy8tDly5dEBcXZxmzZcsWTJ061XKcvLw8ZGZmAgBSU1NhMBiwZcsWS+BTXl6O3bt3Y9KkSe58ZAVIywitLbiAtQUXvHwugcMs1BcVOypMnvHFEaevr7lVh4oa99sIOLsROyr2daW0ogYf7zyDj11MzwG3uztndTdICoQWfXvK8v8XfHMSsVFhmCdhzzDrIu+WzXSAAFypqJY9VSZ1Cw3uRk9E/kJ2IGQ2m7Fs2TI88cQTlroeADh9+jQ++eQTDB06FK1atcLPP/+MefPmITIyEiNGjAAADBkyBC+//DImT56M559/HmazGfPmzUNoaCjuv/9+AMDYsWPx5ptv4umnn8b06dNRWFiI9957D/Pnz7e81wsvvID77rsP//u//4ucnBysXr0a+/btsyyx12g0mDp1Kv7yl7+gU6dOSE1NxcyZM5GcnIxRo0Z5cr3c1j+lBRbglOuBZGPRt6ew6NtTdutspHTrdjcIcnUjVqp3kdTpOVeBgyNlN2sxccUBLHFS/OwqqyVlu4+GgY+ruh6p9VHcg4yIvE12jdDmzZtx7tw5TJgwwebxiIgIfPfddxgxYgQ6duyIxx9/HM2bN8f333+PhIQEAEDXrl2xfv16HD58GJmZmbj33ntx4cIF5ObmWqaw9Ho9/vOf/6C4uBh9+/bFSy+9hDfeeAPPPvus5b3uuecerFy5Eh9++CF69eqFzz//HGvXrkVaWpplzCuvvILnn38ezz77LO6++27cuHEDubm5iIjwTapdG+LeL3Sx582T97RHM13T3RHFXnfi/FNXvfJerm7EdWYBy3cWq9bFWpyeG313O7cDL0crsKR05HbWGdqTPkDu9F4iIlKaR32Egp2SfYTk9Loh+8QszY7pgxGi1eCd3B+w8FvXWbZmuhBUVNc5DCK0GtvMjLMMiC/2M5uW1Qmr9/7k8XuueibDJlNTZxYw8K2tko7b8NoDyvUBktN7iYhICjn376abYlAZiz49Zz1VZKqswco95yS9bsKAVLy/9WSjx8Vb7YIxfRAXHe7yRuxuTZC7xM7Q8zcrszVMw8JvOR25Gy5nd9UHSM7u9dyNnoh8iYGQSuqXQIejtKLG16cS8PKOGbFs5xlJAUlUeAj+ueus3ef0EguJAe/uZ2aPGDpU31Juv7iGwbg7S9PF17APEBEFi+DbntxPhWg1+MvINNcDPWSI0WHB6HTERUnrOxOI1hZckByQ3Kypc1hQLWdJvdz9zDyljwpDTs8k3Kyx3x9JLnv7hrmTpRRfwz5ARBQsmBFSkdaLYeeU+ztgQMdWuHKjGjO+OIIb1be892Y+JiWrFhsZAmi0LoOd2euOSpq+kXND10eGwlTp2fU33azF1h8ueXQMa/YKv+WsRGu4io59gIgoWDAjpBJxakVp4n5U04Z0wbcnLuL5VQeDOgiSalxGiqSMj7G8ulH3aLFT9JcF5y2doKXe0GfmdMOBmUPx3KBU14OdEABFskFxUWEOl8476wZtzd4qOqn7pLEPEBH5O2aEVOKtqRUB9cuqvzpcgg+2Fyt+/EClcXprt3XpepVl5dLmY0asKThv09U7SR+BmTndJDUAfHJA/b5or47ojl5t4vD6l4VerQuLCg9BTESYzT5rzXQhuLdTK4zPaO+ys7SjbtDW7HWG9pc+QFxxRkSe4vJ5J7h8PvCIAcn/PNoL4/6+W9JrpmV1xuq95xwGAuJt9dlBqfjwl2DT3o3f3nLxmltmvPL5Ia91C5+W1QlTBnfyOBiw11n60o1qlN6oRnx0OAz6SLvHlbPJrFyughxvvjcRBTY5928GQk4oGQh99+Nl/O7jPR4dQwMgp2cSNhx23ayuKbIOSIZ0N2DAvK1Od6QH6ut5yitvSSq+jo0Mw5P3pGD13nMwlldbHnd08/V2z6G4qDDse32IVzIg9s7dEBOBMf3aIaVllE1g4o2sjKsgR6keRkQUnNhHyA8dKyn3+BgvPNAJn+z7SYGzCU7x0eEYmZ4MfWQ4AGD2Q92dbsgK1G/HIvUvgbLKWry7pQiGmAhMy+rcKCCwpkTPoejwEFQ4qBPSAJj7cA+vBUH2zt1YXoX5m3+0fG8dmIhL5JUIihy+/y8drheO7Y05G48r0sOIiIgZISeUzAg9vXwPtvxw2aNjDE8z4KtCo0fHaCrio8Pwl5Fp0Go1mPHFkUaF0810oRjQsQW+PnpR9rEbZp4a3vgBSO7YLIWczteektttGridfVFiqsrV+2sAxEWH2dRwOdKwkzYRNR3MCPmh05crPD7GNyeUW04d7EoravGHlQfx3KBU7H99CHadvor8U1dx+vJ15J++ims3b7kVBAG3sw4zvjiC2euO2Uy/xUeHo39qnKLTYeKfKhMGpGBIdwP6to/D/rPX8GXBeSQ0j7B8r8TUlNxu02L2xWwGJq90nMWROlUlpVGjlCAIYA8jIpKGgZBKonUhHh+jqla5LsNNxQfbi9GrTRxG9EyC6WYNFnzTeKsNdwgQGzLa3pRLK2rwVaF7AZaz99IA+KrQiLvax+G+d76xCRYaZozEbNiInsmy30tu8CB2kH79y0JFpqqUDF7Yw4iIpGAfIZX0ahvr61Nosl7/shDrD13AlFUHfX0qbhMDjj+sPNgoY9JwU3kxGzZ3k/y+Ve4GD85aBFhvt6HU+8dHh7OHEREpgoGQSl7J7ubrU2iySitq8Pyqg40ChmD3wfZibJK5wtBVo0RPSMn2SG3UKG5X03Ccmj2MiCg4MBBSyef7udqL1Dfzy0LUyYgApXabFmlQPxUnhZRsj7P3tw5yRvSsbwRp0Nse06CPkL103l4ncSJqOlgjpJKzpTd9fQrkp8QbfLQuVPHtUa5W1MjeAV5Kt2ng9nn/ZWQa5mw87rLrttSpKkfv37DD9bC0JLur9uRkgtiUkYgYCKmkbVyUr0+BFKTR3F7NJVfD4mbxBr+nuBQf7zyjyPlZc6cAuWGQceZKBVbtsW0kaR2YaLUah9tt1G8D0xYbDl+QHKxIDXJCtBq3l8i76lfEpoxETQMDIZV0Tmjm61MgBbkTBIm38AVjeiMuWtfoBq+PDPdKIORuAXTDIMPZVh6Osjj6qPpps/mbiyyPSc24eBLkuCJuguxspdtrawpRWVPncHsRIgoODIRUsvfsNV+fAnlBw+yOM7FRYZj7cA+HAYBYKKxkD6IW0eGKrZ5yFZg0ziLdxLubf/TLjIuUfkVXK2ow7dNDADhdRhTMWCytGhZgBiOzAMzM6Yan7mnvsrhYF6rFkO4Gh8+LhcIa2C8U1qB+t3k5RqYnq5rJEIOlX/dMxuq95xxmXID63kK+KkyWO10oBm+5hdznjyjYMBBSSeYdLX19CuQlLZvrMPTOJJehrrG82mUvHXGKydFqqL891kvW0nZngZc3Scm4SO0t5A1ypwv9IXgjIu/g1JhK7mZzt6CV0DxCcoZByjhXhcKLx/dptLWHPe42FVRi41Qlr4c3iNOQjla62WMdvPlyDzMlfj5EdBsDIZXkn7ri61MghVkvC5ea2ZCaiXBWjyMGSgu2FtkUIVufF+BeU0GllpNL/Zy+2gZDnIa0t9LNFV/uYcbl/kTK49SYShZvk7fHlT4y1CvdfUlZYrAhtSOykoXLL2R1xpLxfZCkQFNB4PZy8oZTWu7Ux6h9PdzhaBrSFV8Fb0r+fIjoNo0guNsNJfiVl5dDr9fDZDIhJibGo2P1/vN/cO2mtF2zASCnRyI2HanfvJM/IM9oNEBkmBY3a2w3rY0MC0FlbZ1bx9RqgAVj+mBEz9vBhnijAhr30gGAhWPtL5uXwtl0iBJTJXVmAQPf2uqwrkfMfu2YPljysV1dD3/p0yNeP2N5FeZsOOpwd/uG10DNKSpv/HyIgpmc+zenxlSiC5WXfNt45CKeG5SKdYdKFF1O3RQJAhoFQQBQ5WYQBNSvFouLDrd5zFlH5Id6JWHOxuM2j8dHh/+yS7zzYMDVdIgS/XbkFDdLfS+pHaJ9zfr6RYZpnQZvYgZQ7Skqb/x8iKgeAyGV/KpLK6ze+7Os16zccw5v/PpOfH/yCtYUXPDSmTVdnmba7NWK2Ct0vlZRg8krG3cwLq2owR9WHsCvC5MwpHui3ayCWt2PvVXcrMQ2GGqSErz5oiO1t34+LLwmYiCkmuzuBtmB0PWqOrz8+WEvnRFZk1swCziuFRFrhvYUl8JoqsScjcedHnvD4RJs+GWXeOusgpTux7PXHUXziDBcuVHt0Y1MqeJmRzfWQMpSOAvepPxM3lx/DEO6GxQNKLxRfM7Ca6J6DIRUsu8cO0v7s0n3dcAFUyVu1tzCXe3isXRnMS5dr3ZrE1F7NxiprLMK+shwl9MhxvJqjPv7bstjhhgdxvRrh5SW0WjZTAcIwJUK10GSq+XkDT+zvYAn75gxaG6sjoI3X01Ryf35uMJ91ohuYyCkkp9LK319CuTEom2nLP//yPlyjOqdjA+3F9vdRBRwvDTd0Q1GKuuswivZXWS/3lhebXdJPeA8KHG2nFxKfUxsVBjK7CwGCLYbq6/6I8n5+bjiq6wWkb/i8nmVXPZh7xGSx2iqwofbi/HsoNRGS6sTY3SYmtUJ1bfMyD911abLsLMbjBxiVqG0osbDI9lytczaVVdr6/qYhlkRe0EQEHwdmX3ZH0nKz0cKf+/6TaQ2ZoRUEh7Kv6wChfhX8bpDJdj28v3Yf/aaZRPRVXvO2WRcYiPD8NSAVEwZ3NHlDUau+GY6h5kWd0j5a9/d+hhX7xssK5qUnqKSS4nic3/v+k2kNgZCKjlfxl8qgUS8ee8/ew2ZHVogt7DE7k7qZZW1mL/5Ryz7vhiP9G6t6Dmcu1qhWBAkkhKUuFsf40ow3FiVnKLy5Bw8CSj9ves3kdo4NaYSDftE+5URaYmSxl26XiUpE1J2sxZLd55R5NwAID46DB/vLFbseA25E5R4GsgEy41VqSkqXwmErt9EamJGSCVt4yPx46Ubvj6NoNCnnR4Hzpk8OsYuGXuDycmEaDX1DRw9rYZx1N1YKe4EJe4GMt6eLvKFQOuPZM0fslpE/oQZIZX0bRfn61MIGgfOmdBMF+LRMUorap0ew/qvYjmZELMCQZA3efLXvqtMgqP3A4LzxipOUY1Mb43MDi0C6vMFelaLSEnMCKmkpDzw6yP8iSfbY4huVNs/RsObt7endJpHhODh3q3xz13noMbOf+4GJa4yCQIaL6P3t+006LZAzmoRKYmBkEpumRvvdUXuu+XFy9nw5i1mQry159vou9vio+/OeOXY1ppHhOCd3/byKChxtQWFvRsrAOSfusqbrR8KtK7fRN7AQEglN6o8z2CQd8VGhmHhuD7IuMN2msM6E6J0wuaZe1Mt22t426j0NhjS3eBxUOIqk2B9Y+U2DkTk7xgIqaTExM7S/q6sshZajcZhf53F4/tgxhdHFFnS3iI6HHNGpiEuOhwffSd/dZguVIsh3ROx78w1GCVOu1bW3MLAt7YqEpRIySS4s40DNwElIrUxEFJJeAjr0gOBs8JoMROyYGsRlu08g7JKeQHRlPs7olNiM5sb/JcF5906z+pbZmw8XIL/G52ON9YdQ+lN112oPz/Q+L28tQWGO9s4MHtERL7AQEglLZqF+/oUSIKWzXROnw/RavBCVmdMGdzJkrn47sfLdoOMhsJCNBiZbtt00ZNCbAHAG+uPotSDDJX1FhhK7i0ld3NStTYBZcaJiBpimkIlbKgYGF76tMDhXlzWxKmhX/dMxo6TVyUde9Wec43223JnSbo1pfoNKb23lJxtHFxljwBl9irLLSzBwLe2YsxHu/DC6gKM+WgXBr61VdLPm4iCFwMhlVxVeANN8o6L5dVONyZtaE9xqeQaHWN5daNgQyzE9gcXypSrY5OzjYMam4A62izW1Ua0RBT8GAipJCrcswaApA65GQi5207YGz8sLQnPDkqVdRxrSuUaZ68/qlhAIGcbB29vAqpWxomIAhMDIZX0aR/r61MgieRkIOTW+LSMblyDVGcW8Mm+n2Udx5pSt+/rVbcUy45YZ7oaBkPuNqx0t55KjYwTEQUuBkIqCdHwUgca6wxEnVlA/qmr+LLgPPJPXbVkD/qlxsMQI/0G/dJnhxoFGgu2Fim+y7wnlMqOSN3GwdubgHoj4+To3wMRBR6uGlPJ2dKbvj4FkknMQDhb1g0AVbekN8u8WG67CqrOLGCZgrvWe6rhai5PSd3GYfTd7TB/84+NXq/EXmVKZ5y4zJ8ouDAQUslF7jUWULQa4FpFjdNl3RNXHJB93IY9dPYUl8ruR6QGcTWXEkvNnTVftBdUWFNirzIx42Q0VdmdRtT88j5SMk5qLfMnIvUwEFKJWeBeY4HELACTVx6APirMaZGtO6yzLlJXnKntzJUKp12olQiSHAUVomlZnTBlcCeP+/y42iwWkJZxcqdJJBH5PwZCKqmqYSAUaATAq7U7m48ZkRwb6bXje2L+5qJGj4lZj2cHpeLLggswlldbnjPE6DD7oTttsiHOgiVnQYVo+fdnMGVwJ0U+j6vNYqVkceQ2iSSiwMBASCXsLE0NLd15BoO7tPT1aUgmBi0fbG+8N5qxvBoTVxzAkl+mhlzV0bgKKgDg2s1aLNhahBeyOity/lLrlRzx9jJ/gJ2viXyBgZBq+MuMGtt64oqvT0FRM744ArO5flrRWR1N9S1pGdJlO88oMj0mkrJZrCPeXubPImwi3+CabpVcuVHtehBRgCu7WYuXPz/ksnmhvX5Kdo9XWet2fx+ll7h7c5k/O18T+Q4zQiq5ykAo4GgAxEaF4Zof9fgJBBU1jtsJiHU00ACxkWGSVszJ7e+zp7gUeceMWFtwAaVWW9t4ml1Rquja3jmzCJvId5gRUkkzHbfYCERzH+6BJeP7IJo/P0VduVGNpwZI21ZETn8fcVPVj3eesQmCAGWyK1KbRMrBztdEvsWMkEruSGiGAz+V+/o0SKLYqDD8dVQa9JHh2HzMiFsSa1oCXcNMh7e0bKbDr3sm44Ptp3DTQQZJ7O/Tt30cdp68gvxTVwEIyLyjJTI6tLDJjrhaig8ol13xtOi6ITWKsInIMQZCKjl+4bqvT4FkeDIzBXM2Hne5sinY6KPCnLYM0IVqJRc6OyUAeceMDoOgX4bgoV5J6PfXzTbntOCbU4iNCsO8h3tYehq5WopvfUwllrh7UnTdkLeLsInIOQZCKrnEGqGAERsVhve2FKmSGfEHgzq1xCN926BltA4vfXYIgP1ASAMgKjxEkUBo83Ejviq86HRMdHiI3aX6QH1RtrhcXx8ZLjtg9UV2xdHSeCU7XxORfKwRUklic2mrZMj3am+Zm0wQBAC/6d0aI9NbQ6vVOO10LaC+t8+0rE6NNppN0kdg0dg+iI+W1i9r2fdnXXbVdlZ0LZq97qhb3bnVzq5Y1y+9sLoAYz7ahYFvbUVuYYmlCBto3GRDib3WiMg5WYFQSkoKNBpNo6/JkycDAJ577jl06NABkZGRaNWqFUaOHIkffvjB8vrly5fbfb1Go8GlS5cs47799lv06dMHOp0OHTt2xPLlyxudy8KFC5GSkoKIiAj0798fe/bssXm+qqoKkydPRosWLdCsWTM88sgjuHjR+V+g3vT8oI4+e2+SR8oNOJgY9PXdraUGFCkto7FzxmCseiYD741Ox6pnMrBj+mCM6JmEv4xM8+apNmIsr0apjGyrpzvZu0PK0nhvFGETkTSypsb27t2LurrbN4nCwkIMGTIEjz76KACgb9++GDduHNq1a4fS0lLMnj0bQ4cORXFxMUJCQvD4449j2LBhNsd88sknUVVVhYSEBABAcXExcnJyMHHiRPzrX//Cli1b8Pvf/x5JSUnIzs4GAHzyySd48cUXsWTJEvTv3x/vvvsusrOzceLECctxpk2bho0bN+Kzzz6DXq/HlClT8PDDD2Pnzp3uXy0PfHsquBrnUXAQg4LcwhLM2XBU0msSmkc4rJEZ0TMJz/2c6nBKyxvio8OdTi2JfJFdkbM0XukibCKSRiMIgtuzAFOnTsWGDRtQVFQEjabxf6yHDx9Gr169cPLkSXTo0KHR85cvX0br1q2xdOlS/O53vwMATJ8+HRs3bkRhYaFl3OjRo1FWVobc3FwAQP/+/XH33XdjwYIFAACz2Yy2bdvi+eefx4wZM2AymdCqVSusXLkSv/3tbwEAP/zwA7p164b8/HxkZGRI+nzl5eXQ6/UwmUyIiYmRd3Ea+NXbW3GmtNKjY1DTodbqrSXj+wCAyxVXwO1alR3TBzu8OYt1MEu+LcK2oqvKnqwDq57JgKmyBpNWHADg+Lr5oktz/qmrGPPRLpfjVj2Twf3JiBQk5/7tdo1QTU0NVqxYgQkTJtgNgioqKrBs2TKkpqaibdu2do/xz3/+E1FRUZZgBQDy8/ORlZVlMy47Oxv5+fmW992/f7/NGK1Wi6ysLMuY/fv3o7a21mZM165d0a5dO8sYe6qrq1FeXm7zpZQb1bcUOxYFt2lZnRpNkShNqwEWje2DId0NklZcScmmWNfBqBUEGWJ06Jcab5la0keFNRoTHR6CaVmdsWP6YNWnmLg0nsj/uR0IrV27FmVlZXjyySdtHl+0aBGaNWuGZs2a4auvvkJeXh7Cw+0XUC5duhRjx45FZOTtHbiNRiMSExNtxiUmJqK8vByVlZW4cuUK6urq7I4xGo2WY4SHhyM2NtbhGHvmzp0LvV5v+XIUwLmjXXy0Ysei4BUXFYa72sfj5aFd0EznvbUMC8b0xoie0jY/Beqnn5zVqjiqg/G22Q/daROYmews/b9ZU4d3N/+IvGOO/9v3Fi6NJ/J/bv+mXbp0KYYPH47k5GSbx8eNG4eDBw9i27Zt6Ny5Mx577DFUVTX+5Zifn4/jx4/j6aefdvcUFPfqq6/CZDJZvn766SfFjj24a4JixyLf81bVRtnNWoxbuhsvfnYIN6qVb+KYpI/AkvF9MKJn/X+3UjMRr+d0cxgEyenjo5TYqDDLTveuzsF6jzNP9xuTy5v7kxGRMtzqI3T27Fls3rwZX3zxRaPnxGxKp06dkJGRgbi4OKxZswZjxoyxGff3v/8d6enp6Nu3r83jBoOh0equixcvIiYmBpGRkQgJCUFISIjdMQaDwXKMmpoalJWV2WSFrMfYo9PpoNN5Z5n7mVI2VAwm3rqdeuO4M3O6oWVznd3iW6mZCHFlmT1Ss0ruEOuS3n6kJ3YXl8JRZ2mp21TsOn0VWo1GtWJkb+1PRkTKcSsQWrZsGRISEpCTk+N0nCAIEAQB1dW2y1tv3LiBTz/9FHPnzm30mszMTGzatMnmsby8PGRmZgIAwsPD0bdvX2zZsgWjRo0CUF8svWXLFkyZMgVA/eq1sLAwbNmyBY888ggA4MSJEzh37pzlOGrb9sNln7wvUXx0OEamt7b7XN/2cYiPDkNpheNu0rGRYTALAurMgt0btrfrW2Y92B33dm6Fezu3cjhG6jlM/tcBm41e1SigFuuX3lx/zCZYM/igeJuIGpMdCJnNZixbtgxPPPEEQkNvv/z06dP45JNPMHToULRq1Qo///wz5s2bh8jISIwYMcLmGJ988glu3bqF8ePHNzr+xIkTsWDBArzyyiuYMGECtm7dik8//RQbN260jHnxxRfxxBNP4K677kK/fv3w7rvvoqKiAk899RSA+qzU008/jRdffBHx8fGIiYnB888/j8zMTMkrxpRmqmpavWnIfzTcfFSUW1iCN9cfcxoEAUBZZS3G/X23w6DBm/Utzw5KlRQoSD2Hhrvdi718vN2rh0vjifyX7EBo8+bNOHfuHCZMmGDzeEREBL777ju8++67uHbtGhITEzFo0CB8//33lt4+oqVLl+Lhhx9uVMwMAKmpqdi4cSOmTZuG9957D23atMHf//53Sw8hAHj88cdx+fJlvPHGGzAajUhPT0dubq5NAfX8+fOh1WrxyCOPoLq6GtnZ2Vi0aJHcj6uYiDAtauoYDJH64ps1nu6VsklpQyUOggZXW0S4SwNg3aESvDKsm8uAwd1zUGojVimU3J+MiJTjUR+hYKdkH6F7/pqHC+X2/zIn8qaGPWrqzAIGvrXV7bqeuKgw7Ht9iOzd393lqseO2Lso75gRH+8843YPJvbyIQoeqvQRInliIri/LanP3ookT4ubr92sxYKtRTaPDUtLwrODUt0+pjPO6n+sexd9vPMMAKBhW7NYO72F5L4PEQUv3p3VYqfpJJE3aQDLZp75p65aalMulHne4XzZzjOYMriTJStUZxaw7lCJrHOTmrVxVP/jKAslrpB/ekAKsrobYDYLGLd0t9vv488c7WhPRNIxEFLJ1RucFiN1iRmahtNgStwmyyprsae41DKVJDfLZNBHYGZON8zZeNxhXY+4dN5ejx1XvYs0ADYVGvGnnPpA0Fn9kLP38Wdisbv1dffFNiJEgY5TYyq5aqfjLZE3rdh1zm63Z6XqeIym25klqdNK/19me6vd6pMtGauGwZmrHjtS+wbtKS4FAIy+u63DIMjZ+/grKTvaE5E0zAipROWGtkSoqPHuKsU5G4/jXGklUlpG4cr1atcvADA8LcmmINlRj53EGB3G9GuH6ltm5J+62mjKR2rglXfMiBc/LXAYNAVSLx9xGsxoqsScjccl7WgfSMEdka8wECIit5RW1GD+5h8t32s0gLM1qHFRYXannxr22Dlz5SZW7TmH+ZtvF2Q3nPKRWs8jFlDbMy2rM6YM7hgQwYK9aTBHrLNhXAVH5BqnxlTi/79qiTzjqhFH/Wqzk/iy4DzyT1212fdL7LGjC9Xi3c0/wljufMrH1R5ermgArN57zs1Xq8vdDW25Co5IGgZCKgnnlfa65Bjv7BNHypm/+Ue8sLoAYz7ahYFvbbWpZZGzcaq4h5e7M84Na4j8lScb2gbiKjgiX+DtWSVcPe99F8ql1amQfxCzPJsOlyD/1FXMzzshqwB6WFoSpmV18ugcviosaZSd8lSdWUD+qat2M19yudPziTvaE8nDGiGVaDTu9rslCk7ifw1TVh2QtZjAesonpWW0R+fwz/yz+Gf+WcWWnSu9pF3u9FagroIj8iVmhFQSGsJfShR4kvQRMMTovFrjJjdhYj3lo9T0jxLLzr2xpF3u5zPoI7y+gSxRsGFGSIKbNbcQWnOr0eNajQYRYSE24xyprDF75dyIvCn7zgT0bheHF1Yf8vWpAAAMMTrLlI/ZLCCtdQwSY3S46OG0qPWy86xuiaipu/3fa51ZwL4z13D5RhVaNYtA/9R4ROlu/+q8WXMLdWYBs9YddVrf9Kc1R1BWWYvE5hG4KyWuUcbG3u8TKZ8vPjoM04d1Q0JzneW41r+LNNAgMvz2cStr6iA4yE43HFtVWwezkyr4qPBQn4+NDAv5JeMOVN+qczoVKWdsRGgItL/8jGpumXHL7Ph3uJyxutAQy89eztjaOjNq6xyPDQ/RIjREK3vsrTqzzb/3hsJCtAhzY2ydWUD1LcctPEK1WoSHyh9rNguokjhWKgZCEvT77y3Q6qIaPX5/l1ZY9lQ/y/d952xGZa39HxDzQRSIln9/Dsu/r19dFRmmRWWtbwP6Hq31lhvD+bJK3Pv2N4odW6xB+q7oCp5avtfhuIhQLd4dnW7JunR/42tJxy+tqMWMfx9x+Hz/1Hh88lym5fuBb32D0grHHenF3yl//U0PzNlwHOcdbJ3SKaEZ8l68z/L9Qwt2oOjSDbtjW8dGYueMwZbvH/sgH4d/NtkdGx8djgMzh1i+f+LjPdjtoPg8MiwEx+cMs3w/acV+fHPist2xAHBmXo7l/7/4aQE2HTE6HHvsz9mWwOlPXxTi3wd+djh2/+tZaNGsflHFXzYcx//bddbh2O9euR9t4+t/7//Pf07gw+2nHY79z7RB6JzYHACw8JuTeG9LkcOxX04egF5tYwEAy3YWY+5XPzgca70R8Ko95/DGl0cdjv34ybswuGsiAGDtwfN4+fPDDscuHNsHOT3r//1+ffQiJq884HDsO7/tiUfvagsA2F50GROW73M49s8j78T/l5kCoL6+bcxHuxyOfXV4Vzx3XwcAQOF5E0Yu3Olw7AsPdMK0IZ0BACcv38DQ+dsdjn120B3404huDp+3h1NjKmF1EAW6x+5u6+tTQGqrZl5/j8s3nNflVN0y+0X3Zk6DESlDIwiuun80XeXl5dDr9Si5fBUxMTGNnpczNXbnG18zGKKAFR8Vhu+mD8aAt7aiTMXtYp4bdAcy7mhhmfKxTntvOnIBb64/5vG0WEMrf98fPdrokfW3bQ6PLe5PtmP6YFTfqsPu06VOs0iOJMbosPnF+xCi1bj8fWI9RZfYPBIDO7W0ZMfkTHdxaoxTY01haky8f5tMJrv3b5vXOH2WANT/R2n9H6azcY5wzRgFstKbtSj4qUzV90zSR+CVYV3trn7KLSzB5H8dVPS/KTG46X9HC+wpLnUaYDXs3jyocyunG7s6crG8GoXny+12gLb3++T+rgl2j2MdvLgiZ6x1YBYIY3Wh3hkbHqpFuMQJFG+NtQ4ylBwbahUUKTk2RKuRdN+UO1YrY6xUnBpTCUulKdDln7qqajZo2J312240/KvdkyaDjjRcdi512bo4TmzwaH0sqdgBmsi3GAgRkSSnL19X9f2WfX8GYz7ahQHzttjU47jTZNAVfVSYTb2N1GXr1uPEDWQNenlL3tkBmsi3GAiphKvGKNBtKrzo1uv6p8R59O/fWF6NiVbFyd7IoJgaZLpc7WXmqHvzsLQk7Jg+GKueycD8x3ohPjpc9jGISF0MhFTC+iBqqnafuabIv/8ZXxxBnVnwWgZF3McMcD7V5ap7s7iB7G/6tMFff5Pm1jGISD0MhIgoIJTdrMWuU1c93nneHnubsDqa6pKzbF08RmKM+8cIBkruv0akNK4aIyLVRYWH4GZNnezVlDtPXcaATi0x68HumLjCcRM4dzWcdhuWloQh3euLti9dr0JC8/qpLPlZHNtP2ZS6lii9/xqR0pgRUgmT30S33aypw7SszrILi/+Zfxa5hSUY0t2A2Kgwxc+r4bRbnVnwKAgS9x8zNliKf7G82i+aMnqbN/Zf8xZmrZouZoRUwv+kiGyltIzCjumDsae4FDuKLmPht6dcvuZGdR0mrTiAqVmdFF3KL/YQsi5c9jST4WyZv/XeZkO6G4KyTiiQPj+zVk0bM0JE5BMJzSMshcUvDu0iK8OzbOcZxc7DXuGyEpkMV8v87dUlBZNA+fyBlLUi72AgRESKCAuR9le9vWXjIVoN5j3cQ9LrBQBllcplgxoWLrvKZAC2K8wcTanIbcoYbALh88v9WVNw4tQYESmitk76zcLesvFhaUlYMr4Ppv/7CEwSAp1muhDcqHa855AU8dFh2Pby/ZZ9jAB5mQxTZY3DKRV3mjIGk0D4/HJ+1va2QaHgwIwQEakmPjrM6bLxYWlJWDSuj6Rj3duplcfnU1pRi/1nr9k8JjVDkXfM6HRK5VpFjctl/loNcK2iRu5pBwR3m1KqKRCyVuR9DISISDUzf32ny+LTjDtaSLqBjs9or8g5NbzJSc1QrC244HRKZc7GY5iZ083pMcwCMHllcNaheNKUUi2BkLUi72MgREQW3r4lGWJc31Ck3kDFgMlTDW9yYibDEQ3qM1ulTjI54pRKXLQOC8f2hqt7fbDWoSjRlNKbAiFrFcz8pWUBa4SICLpQLSbd1wGr9/4EY7l3pgGS9BHo2z4O+aeuuuzLI95AG9bfGBosaZ6Z0w1/WHmw0evFI+qjwlwus284NRWi1eChXkn4YHux3fECgJG9WmPZ92ecHhcAviosQfv4KDj7/e7PdSie9lEClGxKqTwx6J604kCj5p7+krUKVv7UskAjNKUWpzKVl5dDr9fDZDIhJibGo2OlzNio0FkReY8hRocx/drh8M9l2PLDZUWP/dygVKw7VCL5F1+dWcCuU1eRf/oKgPpl9nenxGP/2Wu4dL0KZ67cxKo95+wGbuJxzWbBbqDUcOyO6YNtls676lodrQtBhYeF2g29NzodI9NbK3pMT/jTjcrbmtJn9Qdiy4KGwYcYbiqRLZRz/2Yg5AQDIWpq5G55IYVWAzw1oD2W7jhr9/2Axr/47N2YxD5DUhopLhrbByN6JiH/1FWM+WiXy/GrnslAZocWqDMLGDBvq9eyYs5Mub8jBnRs6RfZEjVuVP5GiewXuVZnFjDwra0OV+uJzU2t/zhxh5z7N2uEiMjCG38VDbvTgI/tBEHi+wkAZq87aqkPcNTgruxmreRu0nM21tfcGE2VksaLBdN7ikt9EgQBwIJvTmLMR7sw8K2tPi2ebqq9dUK0GvRLjUdC8whcul4/VRlsn9Ef+GOjTdYIEZFXxEaFofaWGZsKjS7HGsurMf3zw/jrwz0c3oTlKDFVYcHWIvwj334A1pBYMO0Py6RLTFWYuOKAJavlitKZjKbaW2fT4RK8/mWhTRE8p8eU548tCxgIEZHiftunNf594LysgObzAz8j96gRN6pvKXIO8zcXuRzTcI8xf1omPWXVASxAb4zomexwjDdqW/zxRuVtczcds1scX/JLT6hgnAr0FX9sWcCpMSJS3Objl9zK6igVBMlhvSqoX2q8pCX+ajALwB9WHnQ4TeatPbKUuFH5y7JoKTYdvuBwhSBQnwELxqlAX/HHlgUMhIhIcUruBeYt9rpch2g1mP1Qd7ePqdUo34vJ3k3Ym3U8nt6ocgtLMPCtrRjz0S68sLrAL+qeHKkzC3j9y0KX4/xhc9hg4Y+NNhkIEVGT5KjL9bC0JCwa2wfNdI0rB6LDQwDY/wWuAfDMval2n/eEvZuwNwtOPblRBdpO7nuKS1FaIS1oD6apQF/zt0abrBEioibJ0RRYbmEJ5mw8ZjNNFx0egt/fm4r/eqAz8o4ZnTZ67N0uzm7dzkO9khr1UZKq4U3Y23U8UhtaWnOVpdKgPks1pLvBb5aly7k+/lQ/Fgz8qdEmAyEiUlx8dDiuVdR4ZTm+FFHhWlTWmO2+f8MCaWuO+ufcrKnD+1tOoltSjMtf4M6ef2VYNyzfWYw5G4/L+jwNb8JqFJzKvVEF4mozqdenRXQ4t9nwghCtxi/+LTAQIiJFxUSEYtaD3TF1dYHPzuG5QR3w7uYiWdsmyM1oOPsF7uj5EK0GLZvrZH0WezvUi3U8RlOV7GBPDjk3qkBcbSZeR1dZujkj0/wmi0XKY40QESmqvOoWpn1SgE4JzXx2Dikto7FwbB/ERYfbPO6sBkGtRm9yszT2dqj3x4JTf1wWLXK0ik28js6u0nODUiX1c6LAxYwQESnOLAA/Xrrhs/c/c6UCq/f+ZNMcLz46DDNzujksxMw75rrxI+B5RsNVNseRhvU17tTxeJNaWSq5XPVacnQd46PD8JeRaU77OFFw4F5jTnCvMSLv+11GO9yVEo+ii9ex4JtTHh1Lg/qO1tfsbMXhbJ8sKRutisR9yTwh1iIB8rY1sffe/rRHlqPP5as9yuTsmeZP15E8x73GiCggaDX1y9hHprfGgI6tFDmmo8DCUX8dsTZICqUavTlaPuyKdTZKnO7ZcPgCAODXPZOR2aGFT2/e/rQsWm6vJbEeamR6a59fR1IXp8aIyGeeuTcV4aH1f4+5O2Vk7ZE+rfH5gfMOnxfrfJbvLMaTA1IRotW4rA2ypmTdzbC0JAzumoiMuZsl97IR62u8sbWGUvxlWXQgrmIj32BGiIhUp9XUF6G+OuJ2F2dnBcBSRdlpgmjPnI3HLd2Opdb8PD0gRfEgY//Za5KDIDEbFQhNC/0huxKIq9jINxgIEZHq/vFkP5sgSOTulJGofXyU5LHiLu/f/XhZ0vis7ga3zskZOTdhMUj01tYawcafV7GRf+HUGBGprrTSti9Ow0LVbS/fj/1nr+HS9SrkHbuITUdK4OzeLq5I+l1mCv6+o1jW9JqzqTSRtzaBlHoTnpbVCcPSkpB/6qrXpnuCrVjYX1exkf9hIEREqrtyvRp1ZgEhWo3TehddqBYbD5c4DWrEpomj726LrwpLMPrutphvp5miJ2bmeKcnj5S6qPjocEz6VUcA3pvu8eeaI3eJU62TVhyQ1ViTmh4un3eCy+eJvEfcf+vD7cUOg4Do8BBU1NQ5PY4+MhQajQZlVkvmY6PCAMDmMU8osWTeESlL6cWgRB8ZjjEf7XJ5TDnnK2eJeSAKxiCPXJNz/2ZGiIh8wmiqwgfbi52OcRUEAYCp8lajx8pu1kID4Ld92uDzAz+7e4oW3iyoddTQz1rJL4XQC8f2VnS6R4mNUv19Ss1fVrGR/2IgREQ+4e1UtABgx8nLMMRE4GK5+0vyAe8X1A5LS0JtnYDnVx10OEZA/Wq3mTndMXmlMtM9ni4xD5Rsi79s7kn+iavGiChoGcurMaZfO7dfr4H3CqWt1ZkFzFp31OW4ElMVSkyVWDi2tyJNCz2pOQqEZfxEUjAjRERBrV18JBaP74NZXxbi4vUa1y9oQI2C2j3FpTb7ojkzZ+NxJOkjMDOnO+Kiwz2a7nF3ibkSU2pE/oIZISIKaqUVNRiWloT/eTRd1uu0GmDh2N6qTPHIrUEymqoweeUBmCprPGpaKK5ac/RKRxkxOVNqRP6OgRARBbX4ZjrkFpY4rb+xxywAcdE6L52VLbk1SEo1T3TWzdtZzRG7NlMwYSBERD6h+eXrmXtT4c3Zk3NXKzBpxQGUVcpfSq/WjVzMzMihVNbFnY1S2bWZgglrhIhIFRFhWlTVmi3fG6xWF/VuG4s/rJSXsZEiSR+BVXvOub1i7MyVCkXPxxHr5n9yz1WJYE3uEnN2baZgIisjlJKSAo1G0+hr8uTJAIDnnnsOHTp0QGRkJFq1aoWRI0fihx9+aHSc5cuXo2fPnoiIiEBCQoLl9aLDhw/j3nvvRUREBNq2bYu333670TE+++wzdO3aFREREejRowc2bdpk87wgCHjjjTeQlJSEyMhIZGVloaioSM7HJSIF/ffINKx6JgPvjU7HqmcysGP6YEu2YUTPZCwZ30d2VsSVAR1awFhe7fbrV+05p9q+XWJmRu41UCrrImejVHen1Ij8kaxAaO/evSgpKbF85eXlAQAeffRRAEDfvn2xbNkyHD9+HF9//TUEQcDQoUNRV3e7Kdrf/vY3vPbaa5gxYwaOHj2KzZs3Izs72/J8eXk5hg4divbt22P//v145513MHv2bHz44YeWMd9//z3GjBmDp59+GgcPHsSoUaMwatQoFBYWWsa8/fbbeP/997FkyRLs3r0b0dHRyM7ORlUV56yJfCE5LsrpjXZYWhJ2TB+Mfz3dH7GRYYq8p9Td6B0xllerWvArXoNVz2Rg/uPpiI92fB3UWtrviDtTakT+yKMtNqZOnYoNGzagqKgIGk3jyP/w4cPo1asXTp48iQ4dOuDatWto3bo11q9fjwceeMDuMRcvXozXXnsNRqMR4eHhAIAZM2Zg7dq1luzS448/joqKCmzYsMHyuoyMDKSnp2PJkiUQBAHJycl46aWX8Mc//hEAYDKZkJiYiOXLl2P06NGSPh+32CBSRpI+AjumD5aUIcg/dVXSNhJSzMzphjkbj3t0jPdGp2NkemtFzkcuR9tv+NP2F/7eWZqaJjn3b7eLpWtqarBixQpMmDDBbhBUUVGBZcuWITU1FW3btgUA5OXlwWw24/z58+jWrRvatGmDxx57DD/99JPldfn5+Rg0aJAlCAKA7OxsnDhxAteuXbOMycrKsnm/7Oxs5OfnAwCKi4thNBptxuj1evTv398yxp7q6mqUl5fbfBGRZzSQN02iRM2LmC35XWaK0+XhUviy4DcQsi5yptSI/JHbgdDatWtRVlaGJ5980ubxRYsWoVmzZmjWrBm++uor5OXlWYKa06dPw2w2469//SveffddfP755ygtLcWQIUNQU1PfTMxoNCIxMdHmmOL3RqPR6Rjr561fZ2+MPXPnzoVer7d8iQGcEmJ0IYodiyhQJLlxw/Y08LCuUQkP1bqsZXF2HF9OPYmsp8vs1VcRkWfcDoSWLl2K4cOHIzk52ebxcePG4eDBg9i2bRs6d+6Mxx57zFKXYzabUVtbi/fffx/Z2dnIyMjAqlWrUFRUhG+++cazT6KAV199FSaTyfJlnany1Fcv3KfYsYh8JS4qDEvG98Gisb0b1a/ERoZh6gMd8a+n+3t0w5bS5M8Qo8O/ft8fTw9IaXQeDbMlzrIqzw1KtSzjb/gegP8U/DLrQuQ9blUSnj17Fps3b8YXX3zR6Dkxm9KpUydkZGQgLi4Oa9aswZgxY5CUVP+LqXv37pbxrVq1QsuWLXHu3DkAgMFgwMWLF22OKX5vMBicjrF+XnxMfE/x+/T0dIefS6fTQafzTgO11vGRCA/RoKZOnRUoFFzEDTZ1oVpU3zK7Gq7I+wHAfz3QCXVmM4D6G3HGHbdvwtlpSV6pDbFeSu5oY9HZD92JAR1bYkDHlvhTTneX5+FseXjvdnGNNg41+OHGoUTkHW4FQsuWLUNCQgJycnKcjhMEAYIgoLq6fvnqgAEDAAAnTpxAmzZtAAClpaW4cuUK2rdvDwDIzMzEa6+9htraWoSF1f+ll5eXhy5duiAuLs4yZsuWLZg6darlvfLy8pCZmQkASE1NhcFgwJYtWyyBT3l5OXbv3o1Jkya585EV8eN/j0Dn1zZ5NRjSaoCkmAiYKmtxo6bO9Qt8SBeqQfUt5a+FRgMM7tIKEwbcAbMgYOWec9j6wyWHAUS0LgRhWq1Nwz1DjA5j+rVDuxbRKL1Rjfjo8PopGw1w6Xo1rlyvxoGzV7H95FVUVNu/zg1v4s10ITALwE0nP5doXQjG92+HdYdK7N6Yh3Q3YMHWInyw/bTT48RGhmFgp5bYd+YajOVVNo9ndUvAgE6tkNBMB2iArccvYk3BeZRWWH1+CYGAN3f0FrM4UgIUqefhaJzcHjpEFFxkrxozm81ITU3FmDFjMG/ePMvjp0+fxieffIKhQ4eiVatW+PnnnzFv3jzs3LkTx48fR0JCAgBg1KhROHnyJD788EPExMTg1VdfxenTp1FQUICwsDCYTCZ06dIFQ4cOxfTp01FYWIgJEyZg/vz5ePbZZwHUL5+/7777MG/ePOTk5GD16tX461//igMHDiAtLQ0A8NZbb2HevHn4xz/+gdTUVMycOROHDx/GsWPHEBEhrQZByVVj1s6XVmL4+9tQUV2H8BANHrmrDc5cuYnIsFDclRKHrgnNsfdcKQAN7m4fhx8v3cDpKzfwo/E6IsK0SGkZjQe6GrDvTCkKfrqG6ltmtI2PwiN92uCeji0RotVYVnIYy6tQeqMasVHhKK2owdWKavx8rQJnrlQCAHq11ePV4d1x6Kcy5J++AgGAXheG0spqGE3VqDMLKK2ogS5Mg7KKWlTW1qHEVIk6s4DmEWFIaK6DLkyL0hu1CA/VQh8ZCl1oCC7fqIE+Mgz3pLbAiUvluGCqRuvYSNyZFINWMToY9JHolxqPOrOAf3x/BnuKr+JCWSXCQzSoNdff8PqnxmN8RgoKfiqDsbwKV65XobSiBiWmKiTrIxEbFYayyloIgoDrVbXQaLRIaRGF32WmIDzUdta3zixg1+mr+P7UFfxcehMA0CYuCvd0bImMO+pvju7cCC3X2VSJKzeqUVZZC80v2ZO7U+Kx/+w1m2Nav098ZDiOGcux/+w1RIeH4GE7Pz9H51NnFrDr1FXkn74CQIP+qfHQajS4UlFtM17qih5/Xfnjr+dFRP5N1v1bkOnrr78WAAgnTpywefz8+fPC8OHDhYSEBCEsLExo06aNMHbsWOGHH36wGWcymYQJEyYIsbGxQnx8vPCb3/xGOHfunM2YQ4cOCQMHDhR0Op3QunVrYd68eY3O49NPPxU6d+4shIeHC3feeaewceNGm+fNZrMwc+ZMITExUdDpdMIDDzzQ6JxdMZlMAgDBZDLJeh0RERH5jpz7t0d9hIKdtzJCRERE5D2q9BEiIiIiCnQMhIiIiKjJYiBERERETRYDISIiImqyGAgRERFRk8VAiIiIiJosBkJERETUZDEQIiIioibLrb3Gmgqx12R5ebmPz4SIiIikEu/bUnpGMxBy4vr16wCAtm3b+vhMiIiISK7r169Dr9c7HcMtNpwwm824cOECmjdvDo1G2Y0ey8vL0bZtW/z000/cvsMJXifXeI2k4XWShtdJGl4n13x5jQRBwPXr15GcnAyt1nkVEDNCTmi1WrRp08ar7xETE8P/iCTgdXKN10gaXidpeJ2k4XVyzVfXyFUmSMRiaSIiImqyGAgRERFRk8VAyEd0Oh1mzZoFnU7n61Pxa7xOrvEaScPrJA2vkzS8Tq4FyjVisTQRERE1WcwIERERUZPFQIiIiIiaLAZCRERE1GQxECIiIqImi4GQDyxcuBApKSmIiIhA//79sWfPHl+fkldt374dDz74IJKTk6HRaLB27Vqb5wVBwBtvvIGkpCRERkYiKysLRUVFNmNKS0sxbtw4xMTEIDY2Fk8//TRu3LhhM+bw4cO49957ERERgbZt2+Ltt9/29kdTzNy5c3H33XejefPmSEhIwKhRo3DixAmbMVVVVZg8eTJatGiBZs2a4ZFHHsHFixdtxpw7dw45OTmIiopCQkICXn75Zdy6dctmzLfffos+ffpAp9OhY8eOWL58ubc/nmIWL16Mnj17Whq0ZWZm4quvvrI8z2vU2Lx586DRaDB16lTLY7xOwOzZs6HRaGy+unbtanme1+i28+fPY/z48WjRogUiIyPRo0cP7Nu3z/J8wP8OF0hVq1evFsLDw4WPP/5YOHr0qPDMM88IsbGxwsWLF319al6zadMm4bXXXhO++OILAYCwZs0am+fnzZsn6PV6Ye3atcKhQ4eEhx56SEhNTRUqKystY4YNGyb06tVL2LVrl/Ddd98JHTt2FMaMGWN53mQyCYmJicK4ceOEwsJCYdWqVUJkZKTwwQcfqPUxPZKdnS0sW7ZMKCwsFAoKCoQRI0YI7dq1E27cuGEZM3HiRKFt27bCli1bhH379gkZGRnCPffcY3n+1q1bQlpampCVlSUcPHhQ2LRpk9CyZUvh1VdftYw5ffq0EBUVJbz44ovCsWPHhP/7v/8TQkJChNzcXFU/r7vWrVsnbNy4Ufjxxx+FEydOCH/605+EsLAwobCwUBAEXqOG9uzZI6SkpAg9e/YUXnjhBcvjvE6CMGvWLOHOO+8USkpKLF+XL1+2PM9rVK+0tFRo37698OSTTwq7d+8WTp8+LXz99dfCyZMnLWMC/Xc4AyGV9evXT5g8ebLl+7q6OiE5OVmYO3euD89KPQ0DIbPZLBgMBuGdd96xPFZWVibodDph1apVgiAIwrFjxwQAwt69ey1jvvrqK0Gj0Qjnz58XBEEQFi1aJMTFxQnV1dWWMdOnTxe6dOni5U/kHZcuXRIACNu2bRMEof6ahIWFCZ999pllzPHjxwUAQn5+viAI9QGnVqsVjEajZczixYuFmJgYy3V55ZVXhDvvvNPmvR5//HEhOzvb2x/Ja+Li4oS///3vvEYNXL9+XejUqZOQl5cn3HfffZZAiNep3qxZs4RevXrZfY7X6Lbp06cLAwcOdPh8MPwO59SYimpqarB//35kZWVZHtNqtcjKykJ+fr4Pz8x3iouLYTQaba6JXq9H//79LdckPz8fsbGxuOuuuyxjsrKyoNVqsXv3bsuYQYMGITw83DImOzsbJ06cwLVr11T6NMoxmUwAgPj4eADA/v37UVtba3Odunbtinbt2tlcpx49eiAxMdEyJjs7G+Xl5Th69KhljPUxxDGB+O+vrq4Oq1evRkVFBTIzM3mNGpg8eTJycnIafRZep9uKioqQnJyMO+64A+PGjcO5c+cA8BpZW7duHe666y48+uijSEhIQO/evfHRRx9Zng+G3+EMhFR05coV1NXV2fyHAwCJiYkwGo0+OivfEj+3s2tiNBqRkJBg83xoaCji4+Ntxtg7hvV7BAqz2YypU6diwIABSEtLA1D/GcLDwxEbG2sztuF1cnUNHI0pLy9HZWWlNz6O4o4cOYJmzZpBp9Nh4sSJWLNmDbp3785rZGX16tU4cOAA5s6d2+g5Xqd6/fv3x/Lly5Gbm4vFixejuLgY9957L65fv85rZOX06dNYvHgxOnXqhK+//hqTJk3Cf/3Xf+Ef//gHgOD4Hc7d54n8zOTJk1FYWIgdO3b4+lT8UpcuXVBQUACTyYTPP/8cTzzxBLZt2+br0/IbP/30E1544QXk5eUhIiLC16fjt4YPH275/z179kT//v3Rvn17fPrpp4iMjPThmfkXs9mMu+66C3/9618BAL1790ZhYSGWLFmCJ554wsdnpwxmhFTUsmVLhISENFp5cPHiRRgMBh+dlW+Jn9vZNTEYDLh06ZLN87du3UJpaanNGHvHsH6PQDBlyhRs2LAB33zzDdq0aWN53GAwoKamBmVlZTbjG14nV9fA0ZiYmJiA+eUfHh6Ojh07om/fvpg7dy569eqF9957j9foF/v378elS5fQp08fhIaGIjQ0FNu2bcP777+P0NBQJCYm8jrZERsbi86dO+PkyZP8t2QlKSkJ3bt3t3msW7dulmnEYPgdzkBIReHh4ejbty+2bNliecxsNmPLli3IzMz04Zn5TmpqKgwGg801KS8vx+7duy3XJDMzE2VlZdi/f79lzNatW2E2m9G/f3/LmO3bt6O2ttYyJi8vD126dEFcXJxKn8Z9giBgypQpWLNmDbZu3YrU1FSb5/v27YuwsDCb63TixAmcO3fO5jodOXLE5hdOXl4eYmJiLL/IMjMzbY4hjgnkf39msxnV1dW8Rr944IEHcOTIERQUFFi+7rrrLowbN87y/3mdGrtx4wZOnTqFpKQk/luyMmDAgEatPH788Ue0b98eQJD8Dvd6OTbZWL16taDT6YTly5cLx44dE5599lkhNjbWZuVBsLl+/bpw8OBB4eDBgwIA4W9/+5tw8OBB4ezZs4Ig1C+9jI2NFb788kvh8OHDwsiRI+0uvezdu7ewe/duYceOHUKnTp1sll6WlZUJiYmJwu9+9zuhsLBQWL16tRAVFRUwy+cnTZok6PV64dtvv7VZznvz5k3LmIkTJwrt2rUTtm7dKuzbt0/IzMwUMjMzLc+Ly3mHDh0qFBQUCLm5uUKrVq3sLud9+eWXhePHjwsLFy4MqOW8M2bMELZt2yYUFxcLhw8fFmbMmCFoNBrhP//5jyAIvEaOWK8aEwReJ0EQhJdeekn49ttvheLiYmHnzp1CVlaW0LJlS+HSpUuCIPAaifbs2SOEhoYK//3f/y0UFRUJ//rXv4SoqChhxYoVljGB/jucgZAP/N///Z/Qrl07ITw8XOjXr5+wa9cuX5+SV33zzTcCgEZfTzzxhCAI9csvZ86cKSQmJgo6nU544IEHhBMnTtgc4+rVq8KYMWOEZs2aCTExMcJTTz0lXL9+3WbMoUOHhIEDBwo6nU5o3bq1MG/ePLU+osfsXR8AwrJlyyxjKisrhT/84Q9CXFycEBUVJfzmN78RSkpKbI5z5swZYfjw4UJkZKTQsmVL4aWXXhJqa2ttxnzzzTdCenq6EB4eLtxxxx027+HvJkyYILRv314IDw8XWrVqJTzwwAOWIEgQeI0caRgI8TrVL2NPSkoSwsPDhdatWwuPP/64TW8cXqPb1q9fL6SlpQk6nU7o2rWr8OGHH9o8H+i/wzWCIAjezTkRERER+SfWCBEREVGTxUCIiIiImiwGQkRERNRkMRAiIiKiJouBEBERETVZDISIiIioyWIgRERERE0WAyEiIiJqshgIERERUZPFQIiIiIiaLAZCRERE1GQxECIiIqIm6/8HZM+S6/n8DnwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "HDsToDraw = set(populatedTractList).difference(set(envelopeVs+coreVTDs+skipList))\n",
    "availVTDs = list(HDsToDraw)+envelopeVs\n",
    "for t in availVTDs: #excluding VRAcore and skipList; originally populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "for kkkj, t in enumerate(HDsToDraw ):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in HDsToDraw],[HDvPop[t] for t in HDsToDraw] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "41b4b4e2-69fd-4829-84eb-d9255376ca1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the VRA-respecting polygon-based results to a file; uncomment last line to save to file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "DATE = \"10Jul\"\n",
    "print(\"Now we will write the VRA-respecting polygon-based results to a file; uncomment last line to save to file\")\n",
    "HDweight = [tractPop[t]/statePop for t in range(nHDs)]\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "isVRAcore, isVRAenv = [0]*nHDs,[0]*nHDs\n",
    "polyHDtractList = [HDtractList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "for t in coreVTDs+envelopeVs:\n",
    "    HDtractList[t] = VRAvtdList[t].copy()  #overwrite\n",
    "    HDvPop[t] = np.sum([tractPop[v]  for v in HDtractList[t] ])\n",
    "    HDarea[t] = np.sum([tractArea[v] for v in HDtractList[t] ])\n",
    "    if t in coreVTDs:\n",
    "        isVRAcore[t] = 1\n",
    "    if t in envelopeVs:\n",
    "        isVRAenv[t]  = 1\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList,\n",
    "                    \"isVRAcore\":isVRAcore, \"isVRAenv\":isVRAenv}) \n",
    "\n",
    "outname = STATE+str(int(nHDs))+\"VRA_\"+str(int(nDistricts))+\"nW4_\"+DATE+\".csv\"  #solveWedgeC\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "ea5e42fb-a6f7-4c1b-9a8a-99dc48d869e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "like nonVRA states, create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#copy from vanilla HD for converting to unit lists, discontiguities, equalizing pop, equalizing use ...\n",
    "print(\"like nonVRA states, create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "coldRestart = False\n",
    "if coldRestart:\n",
    "    infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                       \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "    shapeDF = pd.read_csv(infilename)\n",
    "    HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "    HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "    HDarea = shapeDF['HDarea'].to_list()\n",
    "    #tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "    nHDs = len(shapeDF)\n",
    "    hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "    hdCPy = shapeDF['centroid y'].to_list()\n",
    "    hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "    HDtractListString = shapeDF[\"HDtractList\"]\n",
    "    HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "    isVRAcore = shapeDF['isVRAcore']\n",
    "    isVRAenv = shapeDF['isVRAenv']\n",
    "    HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "    print(\"OK, I have read in the polygon-based results, ready to assign units\")\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)  #this now includes districts with VRA cores, as they may need triage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "d9f3d82b-6d42-4004-9144-2be3326e021a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vtd avg and sd usage are 0.99993 0.13437\n",
      "here are the tract uses\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops vs avg district pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the sum of HD weight is 1.0\n"
     ]
    }
   ],
   "source": [
    "tractUse = [0.]*nHDs\n",
    "for t in range(nHDs):\n",
    "    for tt in HDtractList[t]:\n",
    "        tractUse[tt] += HDweight[t]*nDistricts\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "\n",
    "plt.hist([tractUse[t] for t in coreVTDs],weights=[HDweight[t] for t in coreVTDs],label=\"VRA\",histtype=\"step\")\n",
    "plt.hist([tractUse[t] for t in envelopeVs],weights=[HDweight[t] for t in envelopeVs],label=\"envel\",histtype=\"step\")\n",
    "plt.hist([tractUse[t] for t in HDsToDraw],weights=[HDweight[t] for t in HDsToDraw],label=\"other\",histtype=\"step\")\n",
    "plt.legend()\n",
    "print(\"here are the tract uses\")\n",
    "plt.show()\n",
    "\n",
    "HDvPop = [np.sum([tractPop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in coreVTDs+envelopeVs],label=\"VRA\",bins=50 ,histtype=\"step\")\n",
    "plt.hist([HDvPop[t] for t in HDsToDraw],label=\"nonVRA\",bins=50,histtype=\"step\" )\n",
    "plt.legend()\n",
    "print(\"here are your HD pops vs avg district pop\")\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"the sum of HD weight is\",np.sum(HDweight) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "783532e9-3cba-48dd-b91e-8c9a0435f7e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2975\n"
     ]
    }
   ],
   "source": [
    "print(nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "d6686f68-0585-49b8-ac4f-5c769615ee60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 730709.144\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 678064.501\n",
      "working on vtd --> unit conversion for HD 2001 last HD had pop 732258.895\n",
      "working on vtd --> unit conversion for HD 3001 last HD had pop 735712.152\n",
      "working on vtd --> unit conversion for HD 4001 last HD had pop 727166.38\n",
      "working on vtd --> unit conversion for HD 5001 last HD had pop 733580.968\n",
      "working on vtd --> unit conversion for HD 6001 last HD had pop 728656.285\n",
      "working on vtd --> unit conversion for HD 7001 last HD had pop 608353.889\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "CCBpopFrac = [ [0. for ccb in CCBlist] for t in range(nHDs) ]\n",
    "for t in range(nHDs):\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block.  Just update max possible use for each CCB\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "d645165b-4c82-42fd-8da3-f26c7c8b9d95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing conversion to units.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 13737.0 would be used 1.167 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 169151.0 would be used 1.809 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 2 with pop 171003.0 would be used 1.781 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 3 with pop 92501.0 would be used 1.827 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 4 with pop 51938.0 would be used 1.607 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 13737.0 would be used 1.1671002922094351 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 169151.0 would be used 1.808736692186495 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 2 with pop 171003.0 would be used 1.781190073905808 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 3 with pop 92501.0 would be used 1.8266947960523408 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 4 with pop 51938.0 would be used 1.6067915024098576 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing conversion to units.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\n",
    "          \"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "fa5648b7-3e18-46a8-b85a-9e61708231d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 0 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 0 is 1.02028 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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QCTuSlJeXp5qaGm3fvv1CygEAABfI7Xbp0rQOujStQ1TDTqyd9y8th8NhPfjgg7r66qs1YMAASVIwGJTX61VycnLE2LS0NAWDQWfMV8POqfWn1jWmtrZWtbW1zvOamprzLRsAALRB5z3DU1BQoG3btmnJkiXRrKdR8+bNU1JSkvPIyspq8X0CANAW1TWE9WzJX/RsyV9U19DGf2l52rRpWrp0qd577z116dLFeT09PV11dXWqqqqKGF9RUaH09HRnzNev2jr1/NSYrysqKlJ1dbXz2L9///mUDQAAzqEhHNbPV+3Sz1ftaru3ljDGaNq0aXrjjTe0evVqde/ePWL90KFDFR8fr1WrVjmvlZeXa9++fQoEApKkQCCgrVu3qrKy0hlTUlIiv9+v/v37N7pfn88nv98f8QAAAGiqZp3DU1BQoMWLF+utt95Shw4dnHNukpKSlJCQoKSkJE2ePFmFhYVKSUmR3+/X/fffr0AgoOHDh0uSxowZo/79++vOO+/UggULFAwGNXPmTBUUFMjn80W/QwAA0OY1K/C8+OKLkqRRo0ZFvP7yyy/r7rvvliQ9++yzcrvdGj9+vGpra5WXl6cXXnjBGevxeLR06VLdd999CgQCuuiiizRp0iTNnTv3wjoBAAA4g2YFnqb8ZE+7du1UXFys4uLiM47Jzs7W8uXLm7NrAP/g+F0WAP/IuJcWAACwHoEHAABY77x/eBAAANjHF+fRWwVXO8u2IPAAAACHx+3S4KzkWJcRdRzSAgAA1mOGBwAAOOoawnr5g72SpHuu7i5vnB1zIwQeAADgaAiHNe+dTyRJdway5bXkYJAdXQAAAJwFgQcAAFiPwAMAAKxH4AEAANYj8AAAAOsReAAAgPW4LB0AADh8cR69OmW4s2wLAg8AAHB43C4FenaMdRlRxyEtAABgPWZ4AACAoz4U1qsf7ZMk3XFVV8V77JgbIfAAAABHfSisWW9tlyTdNrSLNYHHji4AAADOgsADAACsR+ABAADWI/AAAADrEXgAAID1CDwAAMB6XJYOAAAcXo9bv7p7mLNsCwIPAABwxHncuq5vWqzLiDp7ohsAAMAZMMMDAAAc9aGw3vzT55KkW4ZcYs0vLRN4AACAoz4U1vT/u0WSlD8ow5rAY0cXAAAAZ0HgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPS5LBwAADq/HreLvXeEs24LAAwAAHHEet/IHZcS6jKizJ7oBAACcATM8AADA0RAKa+X2CklS3mVpirPksBaBBwAAOOpCYRUs/liStGNunjWBx44uAAAAzoLAAwAArEfgAQAA1iPwAAAA6zU78Kxbt0433nijMjMz5XK59Oabb0asv/vuu+VyuSIeY8eOjRhz+PBhTZw4UX6/X8nJyZo8ebKOHj16QY0AAACcSbMDz7FjxzR48GAVFxefcczYsWN18OBB5/Hqq69GrJ84caK2b9+ukpISLV26VOvWrdPUqVObXz0AAEATNPuy9HHjxmncuHFnHePz+ZSent7oup07d2rFihXauHGjhg0bJkl6/vnndcMNN+iZZ55RZmZmc0sCAABREu9x6+nbBjnLtmiR3+FZs2aNUlNTdfHFF+u6667TT37yE3Xs2FGSVFpaquTkZCfsSFJubq7cbrc2bNig73znO6dtr7a2VrW1tc7zmpqaligbAP4hdHt0WaxLQBsW73Hrn4dlxbqMqIt6dBs7dqx+85vfaNWqVfr3f/93rV27VuPGjVMoFJIkBYNBpaamRrwnLi5OKSkpCgaDjW5z3rx5SkpKch5ZWfZ9EAAAoOVEfYbn9ttvd5YHDhyoQYMGqWfPnlqzZo1Gjx59XtssKipSYWGh87ympobQAwBAC2gIhbVu1yFJ0sjenfml5abq0aOHOnXqpN27d0uS0tPTVVlZGTGmoaFBhw8fPuN5Pz6fT36/P+IBAACiry4U1g8WbdIPFm1SXSgc63KipsUDz2effaYvvvhCGRlf3mo+EAioqqpKZWVlzpjVq1crHA4rJyenpcsBAABtULMPaR09etSZrZGkvXv3avPmzUpJSVFKSormzJmj8ePHKz09XXv27NEjjzyiXr16KS8vT5LUr18/jR07VlOmTNHChQtVX1+vadOm6fbbb+cKLQAA0CKaPcOzadMmDRkyREOGDJEkFRYWasiQIZo1a5Y8Ho+2bNmim266SZdeeqkmT56soUOH6o9//KN8Pp+zjVdeeUV9+/bV6NGjdcMNN2jEiBH65S9/Gb2uAAAAvqLZMzyjRo2SMeaM61euXHnObaSkpGjx4sXN3TUAAMB5sePUawAAgLMg8AAAAOu1yC8tAwCA1ine49bcmy9zlm1B4AEAAI54j1t3BbrFuoyosye6AQAAnAEzPAAAwBEKG32097Ak6aruKfK4XTGuKDoIPAAAwFHbENId/7lekrRjbp4SvXZEBQ5pAQAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYz45rzQAAQFTEud0qGtfXWbYFgQcAADi8cW798NqesS4j6uyJbgAAAGfADA8AAHCEwkbbPq+WJA24JMmaW0swwwMAABy1DSHdXPyBbi7+QLUNoViXEzUEHgAAYD0CDwAAsB6BBwAAWI/AAwAArMdVWgDalG6PLot1CQBigBkeAABgPWZ4AACAI87t1gOjezvLtiDwAAAAhzfOrYeuvzTWZUSdPdENAADgDJjhAQAAjnDYaPeho5KkXp3by23JrSUIPAAAwHGyIaQxz66TJO2Ym6dErx1RwY4uAABoRGv4GYK/zs+PdQltAufwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHldpAQAAR5zbrakjezjLtiDwAAAAhzfOrcdu6BfrMqLOnugGAABwBszwAAAARzhs9HnVCUnSJckJ1txaghkeAADgONkQ0jUL3tM1C97TyYZQrMuJGgIPAACwHoe0gFagNdwPCAD+kTHDAwAArEfgAQAA1mt24Fm3bp1uvPFGZWZmyuVy6c0334xYb4zRrFmzlJGRoYSEBOXm5mrXrl0RYw4fPqyJEyfK7/crOTlZkydP1tGjRy+oEQAAgDNpduA5duyYBg8erOLi4kbXL1iwQM8995wWLlyoDRs26KKLLlJeXp5OnjzpjJk4caK2b9+ukpISLV26VOvWrdPUqVPPvwsAAICzaPZJy+PGjdO4ceMaXWeM0c9+9jPNnDlTN998syTpN7/5jdLS0vTmm2/q9ttv186dO7VixQpt3LhRw4YNkyQ9//zzuuGGG/TMM88oMzPzAtoBAAAXwuN26c7h2c6yLaJ6Ds/evXsVDAaVm5vrvJaUlKScnByVlpZKkkpLS5WcnOyEHUnKzc2V2+3Whg0bGt1ubW2tampqIh4AACD6fHEePXnLAD15ywD54jyxLidqohp4gsGgJCktLS3i9bS0NGddMBhUampqxPq4uDilpKQ4Y75u3rx5SkpKch5ZWVnRLBsAAFiuVVylVVRUpOrqauexf//+WJcEAICVjDH64mitvjhaK2NMrMuJmqj+8GB6erokqaKiQhkZGc7rFRUVuvzyy50xlZWVEe9raGjQ4cOHnfd/nc/nk8/ni2apAACgESfqQxr6k3clSTvm5inRa8dvFEd1hqd79+5KT0/XqlWrnNdqamq0YcMGBQIBSVIgEFBVVZXKysqcMatXr1Y4HFZOTk40ywEAAJB0HjM8R48e1e7du53ne/fu1ebNm5WSkqKuXbvqwQcf1E9+8hP17t1b3bt31+OPP67MzEzdcsstkqR+/fpp7NixmjJlihYuXKj6+npNmzZNt99+O1doAQCAFtHswLNp0yZ961vfcp4XFhZKkiZNmqRFixbpkUce0bFjxzR16lRVVVVpxIgRWrFihdq1a+e855VXXtG0adM0evRoud1ujR8/Xs8991wU2gEAADhdswPPqFGjznoSk8vl0ty5czV37twzjklJSdHixYubu2sAAIDz0iqu0gIAALgQBB4AAGA9O641AwAAUeFxuzT+ii7Osi0IPAAAwOGL8+h/f3dwrMuIOg5pAQAA6zHDAwAAHMYYnagPSZIS4j1yuew4rMUMDwAAcJyoD6n/rJXqP2ulE3xswAwP2rxujy6LdQkAgBbGDA8AALAegQcAAFiPwAMAAKxH4AEAANYj8AAAAOtxlRYAAHC4XS7dMDDdWbYFgQcAADjaxXv0wsShsS4j6jikBQAArEfgAQAA1iPwAAAAx/G6BnV7dJm6PbpMx+saYl1O1BB4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACsxy8tAwAAh9vl0rf6dHaWbUHgAQAAjnbxHr18z1WxLiPqOKQFAACsR+ABAADWI/AAAADH8boG9Xt8hfo9vsKqW0twDg8AAIhwoj4U6xKijhkeAABgPQIPAACwHoEHAABYj8ADAACsR+ABAADW4yotAADgcLtcyume4izbgsADAAAc7eI9+t0PA7EuI+o4pAUAAKxH4AEAANYj8AAAAMfxugZd8WSJrniyhFtLAAAAex0+VhfrEqKOGR4AAGA9Ag8AALBe1APPE088IZfLFfHo27evs/7kyZMqKChQx44d1b59e40fP14VFRXRLgMAAMDRIjM8l112mQ4ePOg83n//fWfdQw89pLfffluvv/661q5dqwMHDujWW29tiTIAAAAktdBJy3FxcUpPTz/t9erqar300ktavHixrrvuOknSyy+/rH79+mn9+vUaPnx4S5QDAADauBaZ4dm1a5cyMzPVo0cPTZw4Ufv27ZMklZWVqb6+Xrm5uc7Yvn37qmvXriotLW2JUgAAQDO4XS4N6pKkQV2SuLXE2eTk5GjRokXq06ePDh48qDlz5uiaa67Rtm3bFAwG5fV6lZycHPGetLQ0BYPBM26ztrZWtbW1zvOamppolw0AAPTlrSX+MG1ErMuIuqgHnnHjxjnLgwYNUk5OjrKzs/Xaa68pISHhvLY5b948zZkzJ1olAgCANqbFL0tPTk7WpZdeqt27dys9PV11dXWqqqqKGFNRUdHoOT+nFBUVqbq62nns37+/hasGAAA2afHAc/ToUe3Zs0cZGRkaOnSo4uPjtWrVKmd9eXm59u3bp0DgzHdm9fl88vv9EQ8AABB9J+pCunr+al09f7VO1IViXU7URP2Q1o9//GPdeOONys7O1oEDBzR79mx5PB7dcccdSkpK0uTJk1VYWKiUlBT5/X7df//9CgQCXKEFAMA/ACOjz6tOOMu2iHrg+eyzz3THHXfoiy++UOfOnTVixAitX79enTt3liQ9++yzcrvdGj9+vGpra5WXl6cXXngh2mUAAAA4oh54lixZctb17dq1U3FxsYqLi6O9awAAgEZxLy0AAGA9Ag8AALAegQcAAFivRe6lBQAAWieXXOqd2t5ZtgWBBwAAOBK8HpUUXhvrMqKOQ1oAAMB6BB4AAGA9Ag8AAHCcqAvp+p+u1fU/XcutJQAAgJ2MjHZVHnWWbcEMDwAAsB6BBwAAWI/AAwAArEfgAQAA1uOkZbSYbo8ui3UJAABIIvAAAICvcMmlS5ITnGVbEHgAAIAjwevRB49eF+syoo5zeAAAgPUIPAAAwHoEHgAA4DhZH9JN//G+bvqP93WynltLAAAAC4WN0ZbPqp1lWzDDAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAelylBQAAIqRc5I11CVFH4AEAAI5Eb5w+fvz6WJcRdRzSAgAA1iPwAAAA6xF4AACA42R9SBN+UaoJvyjl1hIAAMBOYWO0Ye9hZ9kWzPAAAADrEXgAAID1CDwAAMB6BB4AAGA9Ag8AALAeV2kBAIAICfGeWJcQdQQeAADgSPTGaeeTY2NdRtRxSAsAAFiPwAMAAKxH4AEAAI6T9SHd8/JHuuflj7i1BAAAsFPYGL1XfshZtgUzPAAAwHoEHgAAYL2YHtIqLi7W008/rWAwqMGDB+v555/XVVddFcuSWo1ujy6LdQkAALQaMZvh+d3vfqfCwkLNnj1bH3/8sQYPHqy8vDxVVlbGqiQAAGCpmM3w/PSnP9WUKVN0zz33SJIWLlyoZcuW6Ve/+pUeffTRWJUlidkTAABsE5PAU1dXp7KyMhUVFTmvud1u5ebmqrS09LTxtbW1qq2tdZ5XV1dLkmpqalqkvnDt8RbZLgAAX9dSf8vO1/G6BufvYE1NjRq80YsKp3o1Mbj6KyaB57//+78VCoWUlpYW8XpaWpo++eST08bPmzdPc+bMOe31rKysFqsRAIBvQtLPYl3BmWX8rGW2e+TIESUlJbXMxs+gVfwOT1FRkQoLC53n4XBYhw8fVseOHeVyuWJYWfPU1NQoKytL+/fvl9/vj3U536i23LvUtvtvy71Lbbv/tty71Lb7P1PvxhgdOXJEmZmZ33hNMQk8nTp1ksfjUUVFRcTrFRUVSk9PP228z+eTz+eLeC05ObklS2xRfr+/zf3Hf0pb7l1q2/235d6ltt1/W+5datv9N9b7Nz2zc0pMrtLyer0aOnSoVq1a5bwWDoe1atUqBQKBWJQEAAAsFrNDWoWFhZo0aZKGDRumq666Sj/72c907Ngx56otAACAaIlZ4JkwYYIOHTqkWbNmKRgM6vLLL9eKFStOO5HZJj6fT7Nnzz7t8Fxb0JZ7l9p2/225d6lt99+We5fadv//iL27TCyuDQMAAPgGcS8tAABgPQIPAACwHoEHAABYj8ADAACsR+A5g/nz58vlcunBBx90Xjt58qQKCgrUsWNHtW/fXuPHjz/txxP37dun/Px8JSYmKjU1VdOnT1dDQ0PEmDVr1uiKK66Qz+dTr169tGjRotP2X1xcrG7duqldu3bKycnRRx99FLG+KbVEu/9Ro0bJ5XJFPO69995W3/8TTzxxWl99+/Zt1r5aY99N7d/Wz/2Uzz//XN///vfVsWNHJSQkaODAgdq0aZOz3hijWbNmKSMjQwkJCcrNzdWuXbsitnH48GFNnDhRfr9fycnJmjx5so4ePRoxZsuWLbrmmmvUrl07ZWVlacGCBafV8vrrr6tv375q166dBg4cqOXLl0esb0ot0e7/7rvvPu3zHzt2rBX9d+vW7bTeXC6XCgoKJNn93T9X71Z+7w1O89FHH5lu3bqZQYMGmQceeMB5/d577zVZWVlm1apVZtOmTWb48OHmn/7pn5z1DQ0NZsCAASY3N9f86U9/MsuXLzedOnUyRUVFzphPP/3UJCYmmsLCQrNjxw7z/PPPG4/HY1asWOGMWbJkifF6veZXv/qV2b59u5kyZYpJTk42FRUVTa6lJfq/9tprzZQpU8zBgwedR3V1davvf/bs2eayyy6L6OvQoUNN3ldr7bup/dv6uRtjzOHDh012dra5++67zYYNG8ynn35qVq5caXbv3u2MmT9/vklKSjJvvvmm+fOf/2xuuukm0717d3PixAlnzNixY83gwYPN+vXrzR//+EfTq1cvc8cddzjrq6urTVpampk4caLZtm2befXVV01CQoL5xS9+4Yz54IMPjMfjMQsWLDA7duwwM2fONPHx8Wbr1q3NqiXa/U+aNMmMHTs24vM/fPhwxHZaa/+VlZURfZWUlBhJ5r333jPG2P3dP1fvNn7vCTxfc+TIEdO7d29TUlJirr32WucPflVVlYmPjzevv/66M3bnzp1GkiktLTXGGLN8+XLjdrtNMBh0xrz44ovG7/eb2tpaY4wxjzzyiLnssssi9jlhwgSTl5fnPL/qqqtMQUGB8zwUCpnMzEwzb968JtcS7f6NMac9/7rW2v/s2bPN4MGDG13XFj73s/VvjL2fuzHGzJgxw4wYMeKM68PhsElPTzdPP/2081pVVZXx+Xzm1VdfNcYYs2PHDiPJbNy40RnzzjvvGJfLZT7//HNjjDEvvPCCufjii51/j1P77tOnj/P8u9/9rsnPz4/Yf05OjvnhD3/Y5Fqa61z9G/Nl4Ln55pvPuL419/91DzzwgOnZs6cJh8Nt4rt/pt6NsfN7zyGtrykoKFB+fr5yc3MjXi8rK1N9fX3E63379lXXrl1VWloqSSotLdXAgQMjfjwxLy9PNTU12r59uzPm69vOy8tztlFXV6eysrKIMW63W7m5uc6YptQS7f5PeeWVV9SpUycNGDBARUVFOn78uLOuNfe/a9cuZWZmqkePHpo4caL27dvX5H215r7P1f8ptn7uf/jDHzRs2DD98z//s1JTUzVkyBD953/+p7N+7969CgaDEftMSkpSTk5OxOefnJysYcOGOWNyc3Pldru1YcMGZ8zIkSPl9Xoj+i8vL9ff//73Jv0bNaWWaPd/ypo1a5Samqo+ffrovvvu0xdffOGsa839f1VdXZ1++9vf6gc/+IFcLleb+e431vsptn3vW8Xd0r8pS5Ys0ccff6yNGzeeti4YDMrr9Z5209K0tDQFg0FnzNd/KfrU83ONqamp0YkTJ/T3v/9doVCo0TGffPJJk2s5H2frX5K+973vKTs7W5mZmdqyZYtmzJih8vJy/f73vz9rb6fWnW1MLPvPycnRokWL1KdPHx08eFBz5szRNddco23btrWJz/1s/Xfo0MHaz12SPv30U7344osqLCzUY489po0bN+pf//Vf5fV6NWnSJGe7jdX11d5SU1Mj1sfFxSklJSViTPfu3U/bxql1F1988Rn/jb66jXPVEu3+JWns2LG69dZb1b17d+3Zs0ePPfaYxo0bp9LSUnk8nlbd/1e9+eabqqqq0t133+3sz/bv/ilf712y8//3BJ7/sX//fj3wwAMqKSlRu3btYl3ON64p/U+dOtVZHjhwoDIyMjR69Gjt2bNHPXv2/KZKjbpx48Y5y4MGDVJOTo6ys7P12muvKSEhIYaVfTPO1v/kyZOt/dylL29aPGzYMD311FOSpCFDhmjbtm1auHCh8wffZk3p//bbb3fGDxw4UIMGDVLPnj21Zs0ajR49OiZ1t4SXXnpJ48aNU2ZmZqxL+cY11ruN33sOaf2PsrIyVVZW6oorrlBcXJzi4uK0du1aPffcc4qLi1NaWprq6upUVVUV8b6Kigqlp6dLktLT0087c/zU83ON8fv9SkhIUKdOneTxeBod89VtnKuWaPcfCoVOe09OTo4kaffu3a2+/69KTk7WpZdeqt27dzdpX7b0fcpX+2+MTZ97RkaG+vfvH/Fav379nEN6p7Z7rroqKysj1jc0NOjw4cNR+W/kq+vPVUtznav/xvTo0UOdOnWK+Pxba/+n/O1vf9O7776rf/mXf3Feayvf/cZ6b4wN33sCz/8YPXq0tm7dqs2bNzuPYcOGaeLEic5yfHy8Vq1a5bynvLxc+/btUyAQkCQFAgFt3bo14stfUlIiv9/v/E8lEAhEbOPUmFPb8Hq9Gjp0aMSYcDisVatWOWOGDh16zlqi3b/H4zntPZs3b5b05f80W3v/X3X06FHt2bNHGRkZTdqXLX031n9jbPrcr776apWXl0e89pe//EXZ2dmSpO7duys9PT1inzU1NdqwYUPE519VVaWysjJnzOrVqxUOh50/EoFAQOvWrVN9fX1E/3369NHFF1/cpH+jptQS7f4b89lnn+mLL76I+Pxba/+nvPzyy0pNTVV+fr7zWlv57jfWe2Os+N436xTnNubrZ6nfe++9pmvXrmb16tVm06ZNJhAImEAg4Kw/dZnemDFjzObNm82KFStM586dG71Mb/r06Wbnzp2muLi40cv0fD6fWbRokdmxY4eZOnWqSU5Ojjgb/ly1RLv/3bt3m7lz55pNmzaZvXv3mrfeesv06NHDjBw5stX3//DDD5s1a9aYvXv3mg8++MDk5uaaTp06mcrKyibtq7X23ZT+bf7cjfnyJxji4uLMv/3bv5ldu3aZV155xSQmJprf/va3zpj58+eb5ORk89Zbb5ktW7aYm2++udHL0ocMGWI2bNhg3n//fdO7d++Iy7KrqqpMWlqaufPOO822bdvMkiVLTGJi4mmXZcfFxZlnnnnG7Ny508yePbvRy7LPVUs0+z9y5Ij58Y9/bEpLS83evXvNu+++a6644grTu3dvc/LkyVbfvzFfXhXUtWtXM2PGjNPW2f7dP1Pvtn7vCTxn8fXAc+LECfOjH/3IXHzxxSYxMdF85zvfMQcPHox4z1//+lczbtw4k5CQYDp16mQefvhhU19fHzHmvffeM5dffrnxer2mR48e5uWXXz5t388//7zp2rWr8Xq95qqrrjLr16+PWN+UWi7UV/vft2+fGTlypElJSTE+n8/06tXLTJ8+PeJ3GVpr/xMmTDAZGRnG6/WaSy65xEyYMCHid0hs/9zP1r/Nn/spb7/9thkwYIDx+Xymb9++5pe//GXE+nA4bB5//HGTlpZmfD6fGT16tCkvL48Y88UXX5g77rjDtG/f3vj9fnPPPfeYI0eORIz585//bEaMGGF8Pp+55JJLzPz580+r5bXXXjOXXnqp8Xq95rLLLjPLli1rdi3R7P/48eNmzJgxpnPnziY+Pt5kZ2ebKVOmRPwxau39r1y50khqdDu2f/fP1Lut33uXMcY0b04IAACgdeEcHgAAYD0CDwAAsB6BBwAAWI/AAwAArEfgAQAA1iPwAAAA6xF4AACA9Qg8AADAegQeAABgPQIPAACwHoEHAABYj8ADAACs9/8BRPaPFJ/OXFAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 1 I reco 1.02, as we may lose a little later 1.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 1.01249 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 2 I reco 1.02, as we may lose a little later 1.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 2 is 1.04082 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 3 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 3 is 1.02133 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 4 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 4 is 1.02004 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    minCCBuse = float(input(\"enter target CCBuse for CCB \"+str(j)+\" I reco 1.02, as we may lose a little later\"))\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < minCCBuse  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "7b16bdfb-41a7-48c1-99e2-85e28a7267ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After CCB addition, here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 1.01467 0.12299\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"After CCB addition, here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "c712ed2a-de9a-43ec-a031-c419fa369d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.986543641118455\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(unitPop) / aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "a3e4e7ec-a903-47c8-b518-60ba98f41281",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(nUnits):\n",
    "    if len(unitNbrs[u]) == 0:\n",
    "        plt.scatter(u,unitPop[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "ec568e36-5136-4878-ad07-9e87edf85367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 7057 populated HDs out of 7059\n",
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below is the HDvPop histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)\n",
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()\n",
    "print(\"Below is the HDvPop histogram\")\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "aa20836c-50e3-4a5c-99b1-0d79936e04e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 72\n",
      "working on HD 1400 time is now 146\n",
      "working on HD 2100 time is now 206\n",
      "working on HD 2800 time is now 269\n",
      "working on HD 3500 time is now 334\n",
      "working on HD 4202 time is now 414\n",
      "working on HD 4902 time is now 477\n",
      "working on HD 5602 time is now 555\n",
      "working on HD 6302 time is now 620\n",
      "working on HD 7002 time is now 700\n",
      "out of 7057 total HDs, there were 4873.0 contiguous and 3789.0 complement-contiguous HDs\n",
      "1092 HDs had both discontiguity problems, while enclave-only = 2176 and discontig only= 1092\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 2184 2176\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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q3bu3unTpovDwcC1atMhq9/T01NKlS+Xp6am4uDg9/PDD6t+/vyZMmGD1iYmJ0bJly5Senq7WrVvr1Vdf1d///vdqf+QeAGA/AwcOVF5eXnWXUSUq69iMMerVq9cV76c6lfuW2eX4+vpqxowZ1sSw0kRHR19wS+yXunbtqs8//7w85QEAAFQIX+4KAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABs74q+3BUAcI3LOyid+vHqjedfVwq+tr42qWHDhnr66acv+QXhAIEIAGqqvIPSjE5S0amrN6a3v5S89ZoLRdeCJ598Uhs3btSuXbvUrFkzZWVlVXdJ+BkCEQDUVKd+PBeGHvibVO+Wqh/vv19IiwafG5dAVKpHH31UW7Zs0Y4dO6q7FPwCc4gAoKard4sU2abqlwqErpKSEqWlpSkmJkZ+fn5q3bq12xeerl27Vg6HQ6tWrVKHDh3k7++vX/3qV8rOznbbz7///W917NhRvr6+qlevnu6///6LjjllyhS1bNlStWrVUlRUlB5//HHl5+dLklwul/z8/PSf//zH7TWLFy9WQECATp06d7Xt4MGD+v3vf6/g4GCFhITovvvuu+BLZn9p2rRpSk5OVqNGjcp8fhwOh2bOnKnExET5+fmpUaNGF3wh7M6dO/XrX/9afn5+qlu3roYMGWIdj3Tu06h79eql8ePH64YbblBgYKCGDh2qwsKr9x131wMCEQCg2qSlpendd9/Vm2++qd27d2vEiBF6+OGHtW7dOrd+f/nLX/Tqq69q27Zt8vLy0qOPPmq1LVu2TPfff7969Oihzz//XKtWrVKnTp0uOqaHh4emTZum3bt365133tHq1av17LPPSpICAwN1zz33aN68eW6vmTt3rnr16iV/f38VFRUpISFBAQEB2rBhgzZu3KjatWvr7rvvrpKQ8fzzz6t3797avn27kpKS1KdPH+3du1eSdPLkSSUkJKhOnTr69NNPtXDhQq1cuVLDhw9328eqVau0d+9erV27Vu+//74WLVqk8ePHV3qt1zVTQx0/ftxIMsePH6/U/X6ZtcGYcYHn/nuV7fw2z0SPWmp2fptXvhd+97kx4wLP/RdAjXP69GmzZ88ec/r0afeGq/3//XKOd+bMGePv7282bdrktn3QoEGmb9++xhhj1qxZYySZlStXWu3Lli0zkqzjjYuLM0lJSRcdJzo62rz22msXbV+4cKGpW7eutb548WJTu3Ztc/LkSWPMub8nvr6+5j//+Y8xxpj33nvPNGnSxJSUlFivKSgoMH5+fubjjz++7HGPGzfOtG7d+rL9jDFGkhk6dKjbttjYWDNs2DBjjDFvvfWWqVOnjsnPz7faly1bZjw8PExubq4xxpgBAwaYkJAQ63iMMWbmzJmmdu3a5uzZs2Wqo7Jd9D1rqu7v9+VwhQgAUC327dunU6dO6Te/+Y1q165tLe+++67279/v1rdVq1bWzxEREZKkI0eOSJKysrLUrVu3Mo+7cuVKdevWTfXr11dAQIAeeeQR/fjjj9btsB49esjb21sffvihJOn//u//FBgYqPj4eEnS9u3btW/fPgUEBFg1h4SE6MyZMxfUXRni4uIuWD9/hWjv3r1q3bq1atWqZbXffvvtKikpcbut2Lp1a/n7+7vtIz8/XwcPHqz0eq9XTKoGAFSL8/Ncli1bpvr167u1+fj4uK17e3tbPzscDknn5h9Jkp+fX5nH/Prrr3XPPfdo2LBhevHFFxUSEqJPPvlEgwYNUmFhofz9/eV0OvW73/1O8+bNU58+fTRv3jw99NBD8vLysupu37695s6de8H+b7jhhjLXgmsLV4gAANWiefPm8vHxUU5Ojho3buy2REWV/Sm1Vq1aadWqVWXqm5mZqZKSEr366qu67bbbdMstt+j777+/oF9SUpKWL1+u3bt3a/Xq1UpKSrLa2rVrpy+//FKhoaEX1B0UFFTmustq8+bNF6w3a9ZMktSsWTNt375dJ0+etNo3btwoDw8PNWnSxNq2fft2nT592m0ftWvXLtd5rukIRACAahEQEKA//elPGjFihN555x3t379fn332maZPn6533nmnzPsZN26c3n//fY0bN0579+7Vzp079fLLL5fat3HjxioqKtL06dP11Vdf6b333tObb755Qb8uXbooPDxcSUlJiomJUWxsrNWWlJSkevXq6b777tOGDRt04MABrV27Vk8++aS+/fbbi9a5b98+ZWVlKTc3V6dPn1ZWVpaysrIuOxF74cKFmjVrlr744guNGzdOW7dutSZNJyUlydfXVwMGDNCuXbu0Zs0aPfHEE3rkkUcUFhZm7aOwsFCDBg3Snj179NFHH2ncuHEaPny4PDyIAedxywwAarr/fnHNjvPCCy/ohhtuUFpamr766isFBwerXbt2eu6558q8j65du2rhwoV64YUXNHHiRAUGBqpLly6l9m3durWmTJmil19+WaNHj1aXLl2Ulpam/v37u/VzOBzq27evJk2apLFjx7q1+fv7a/369Ro1apQeeOABnThxQvXr11e3bt0UGBh40Tofe+wxt6fn2rZtK0k6cOCAGjZseNHXjR8/XvPnz9fjjz+uiIgIvf/++2revLlVy8cff6ynnnpKHTt2lL+/v3r37q0pU6a47aNbt266+eab1aVLFxUUFKhv375KTU296Jh25DDGmOouoiq4XC4FBQXp+PHjl3yDlte+7Z+o8eKe2nf/MjVufUel7bcsdn13XPdM/0RLn7hDt9Yvx2XZ77Okt+6Uhqw791khAGqUM2fO6MCBA4qJiZGvr+9PDXxS9XXP4XBo8eLF6tWrV4X3MXDgQOXl5WnJkiWVVteVuuh7VlX39/tyuEIEADVVcNS5cGLz7zIDyoJABAA1WXAUAQUoAwIRAADXqMqY1TJnzpwrL8QGmF4OAABsj0AEAABsj0AEADVEDX1oGDXQtfheJRABwHXu/NdanP8uLuBad/69+vOvZKluTKoGgOucp6engoODrS879ff3t77vC7iWGGN06tQpHTlyRMHBwfL09KzukiwEIgCoAcLDwyX99A3wwLUsODjYes9eKwhEAFADOBwORUREKDQ0VEVFRdVdDnBR3t7e19SVofMIRABQg3h6el6Tf2yAax2TqgEAgO0RiAAAgO0RiAAAgO0RiAAAgO0RiAAAgO2VOxCtX79e9957ryIjI+VwOLRkyRK3dofDUeoyefJkq0/Dhg0vaJ84caLbfnbs2KHOnTvL19dXUVFRmjRpUsWOEAAA4DLKHYhOnjyp1q1ba8aMGaW2Hzp0yG2ZNWuWHA6Hevfu7dZvwoQJbv2eeOIJq83lcql79+6Kjo5WZmamJk+erNTUVL311lvlLRcAAOCyyv05RImJiUpMTLxo+y8/efKDDz7QXXfdpUaNGrltDwgIuOinVM6dO1eFhYWaNWuWnE6nWrRooaysLE2ZMkVDhgwpb8kAAACXVKVziA4fPqxly5Zp0KBBF7RNnDhRdevWVdu2bTV58mQVFxdbbRkZGerSpYucTqe1LSEhQdnZ2Tp27FipYxUUFMjlcrktAAAAZVGln1T9zjvvKCAgQA888IDb9ieffFLt2rVTSEiINm3apNGjR+vQoUOaMmWKJCk3N1cxMTFurwkLC7Pa6tSpc8FYaWlpGj9+fBUdCQAAqMmqNBDNmjVLSUlJ8vX1dduekpJi/dyqVSs5nU798Y9/VFpamnx8fCo01ujRo93263K5FBUVVbHCAQCArVRZINqwYYOys7O1YMGCy/aNjY1VcXGxvv76azVp0kTh4eE6fPiwW5/z6xebd+Tj41PhMAUAAOytyuYQvf3222rfvr1at2592b5ZWVny8PBQaGioJCkuLk7r1693+8bm9PR0NWnSpNTbZQAAAFei3IEoPz9fWVlZysrKkiQdOHBAWVlZysnJsfq4XC4tXLhQjz322AWvz8jI0Ouvv67t27frq6++0ty5czVixAg9/PDDVtjp16+fnE6nBg0apN27d2vBggWaOnWq2y0xAACAylLuW2bbtm3TXXfdZa2fDykDBgzQnDlzJEnz58+XMUZ9+/a94PU+Pj6aP3++UlNTVVBQoJiYGI0YMcIt7AQFBWnFihVKTk5W+/btVa9ePY0dO5ZH7gEAQJUodyDq2rWrjDGX7DNkyJCLhpd27dpp8+bNlx2nVatW2rBhQ3nLAwAAKDe+ywwAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANheuQPR+vXrde+99yoyMlIOh0NLlixxax84cKAcDofbcvfdd7v1OXr0qJKSkhQYGKjg4GANGjRI+fn5bn127Nihzp07y9fXV1FRUZo0aVL5jw4AAKAMyh2ITp48qdatW2vGjBkX7XP33Xfr0KFD1vL++++7tSclJWn37t1KT0/X0qVLtX79eg0ZMsRqd7lc6t69u6Kjo5WZmanJkycrNTVVb731VnnLBQAAuCyv8r4gMTFRiYmJl+zj4+Oj8PDwUtv27t2r5cuX69NPP1WHDh0kSdOnT1ePHj30yiuvKDIyUnPnzlVhYaFmzZolp9OpFi1aKCsrS1OmTHELTgAAAJWhSuYQrV27VqGhoWrSpImGDRumH3/80WrLyMhQcHCwFYYkKT4+Xh4eHtqyZYvVp0uXLnI6nVafhIQEZWdn69ixY6WOWVBQIJfL5bYAAACURaUHorvvvlvvvvuuVq1apZdfflnr1q1TYmKizp49K0nKzc1VaGio22u8vLwUEhKi3Nxcq09YWJhbn/Pr5/v8UlpamoKCgqwlKiqqsg8NAADUUOW+ZXY5ffr0sX5u2bKlWrVqpZtuuklr165Vt27dKns4y+jRo5WSkmKtu1wuQhEAACiTKn/svlGjRqpXr5727dsnSQoPD9eRI0fc+hQXF+vo0aPWvKPw8HAdPnzYrc/59YvNTfLx8VFgYKDbAgAAUBZVHoi+/fZb/fjjj4qIiJAkxcXFKS8vT5mZmVaf1atXq6SkRLGxsVaf9evXq6ioyOqTnp6uJk2aqE6dOlVdMgAAsJlyB6L8/HxlZWUpKytLknTgwAFlZWUpJydH+fn5GjlypDZv3qyvv/5aq1at0n333afGjRsrISFBktSsWTPdfffdGjx4sLZu3aqNGzdq+PDh6tOnjyIjIyVJ/fr1k9Pp1KBBg7R7924tWLBAU6dOdbslBgAAUFnKHYi2bdumtm3bqm3btpKklJQUtW3bVmPHjpWnp6d27Nih3/72t7rllls0aNAgtW/fXhs2bJCPj4+1j7lz56pp06bq1q2bevTooTvuuMPtM4aCgoK0YsUKHThwQO3bt9czzzyjsWPH8sg9AACoEuWeVN21a1cZYy7a/vHHH192HyEhIZo3b94l+7Rq1UobNmwob3kAAADlxneZAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yMQAQAA2yt3IFq/fr3uvfdeRUZGyuFwaMmSJVZbUVGRRo0apZYtW6pWrVqKjIxU//799f3337vto2HDhnI4HG7LxIkT3frs2LFDnTt3lq+vr6KiojRp0qSKHSEAAMBllDsQnTx5Uq1bt9aMGTMuaDt16pQ+++wzPf/88/rss8+0aNEiZWdn67e//e0FfSdMmKBDhw5ZyxNPPGG1uVwude/eXdHR0crMzNTkyZOVmpqqt956q7zlAgAAXJZXeV+QmJioxMTEUtuCgoKUnp7utu2vf/2rOnXqpJycHDVo0MDaHhAQoPDw8FL3M3fuXBUWFmrWrFlyOp1q0aKFsrKyNGXKFA0ZMqS8JQMAAFxSlc8hOn78uBwOh4KDg922T5w4UXXr1lXbtm01efJkFRcXW20ZGRnq0qWLnE6ntS0hIUHZ2dk6duxYqeMUFBTI5XK5LQAAAGVR7itE5XHmzBmNGjVKffv2VWBgoLX9ySefVLt27RQSEqJNmzZp9OjROnTokKZMmSJJys3NVUxMjNu+wsLCrLY6depcMFZaWprGjx9fhUcDAABqqioLREVFRfr9738vY4xmzpzp1paSkmL93KpVKzmdTv3xj39UWlqafHx8KjTe6NGj3fbrcrkUFRVVseIBAICtVEkgOh+GvvnmG61evdrt6lBpYmNjVVxcrK+//lpNmjRReHi4Dh8+7Nbn/PrF5h35+PhUOEwBAAB7q/Q5ROfD0JdffqmVK1eqbt26l31NVlaWPDw8FBoaKkmKi4vT+vXrVVRUZPVJT09XkyZNSr1dBgAAcCXKfYUoPz9f+/bts9YPHDigrKwshYSEKCIiQr/73e/02WefaenSpTp79qxyc3MlSSEhIXI6ncrIyNCWLVt01113KSAgQBkZGRoxYoQefvhhK+z069dP48eP16BBgzRq1Cjt2rVLU6dO1WuvvVZJhw0AAPCTcgeibdu26a677rLWz8/bGTBggFJTU/Xhhx9Kktq0aeP2ujVr1qhr167y8fHR/PnzlZqaqoKCAsXExGjEiBFu83+CgoK0YsUKJScnq3379qpXr57Gjh3LI/cAAKBKlDsQde3aVcaYi7Zfqk2S2rVrp82bN192nFatWmnDhg3lLQ8AAKDc+C4zAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABgewQiAABge+UOROvXr9e9996ryMhIORwOLVmyxK3dGKOxY8cqIiJCfn5+io+P15dffunW5+jRo0pKSlJgYKCCg4M1aNAg5efnu/XZsWOHOnfuLF9fX0VFRWnSpEnlPzoAAIAyKHcgOnnypFq3bq0ZM2aU2j5p0iRNmzZNb775prZs2aJatWopISFBZ86csfokJSVp9+7dSk9P19KlS7V+/XoNGTLEane5XOrevbuio6OVmZmpyZMnKzU1VW+99VYFDhEAAODSvMr7gsTERCUmJpbaZozR66+/rjFjxui+++6TJL377rsKCwvTkiVL1KdPH+3du1fLly/Xp59+qg4dOkiSpk+frh49euiVV15RZGSk5s6dq8LCQs2aNUtOp1MtWrRQVlaWpkyZ4hacAAAAKkOlziE6cOCAcnNzFR8fb20LCgpSbGysMjIyJEkZGRkKDg62wpAkxcfHy8PDQ1u2bLH6dOnSRU6n0+qTkJCg7OxsHTt2rNSxCwoK5HK53BYAAICyqNRAlJubK0kKCwtz2x4WFma15ebmKjQ01K3dy8tLISEhbn1K28fPx/iltLQ0BQUFWUtUVNSVHxAAALCFGvOU2ejRo3X8+HFrOXjwYHWXBAAArhOVGojCw8MlSYcPH3bbfvjwYastPDxcR44ccWsvLi7W0aNH3fqUto+fj/FLPj4+CgwMdFsAAADKolIDUUxMjMLDw7Vq1Sprm8vl0pYtWxQXFydJiouLU15enjIzM60+q1evVklJiWJjY60+69evV1FRkdUnPT1dTZo0UZ06dSqzZAAAgPIHovz8fGVlZSkrK0vSuYnUWVlZysnJkcPh0NNPP63/+Z//0YcffqidO3eqf//+ioyMVK9evSRJzZo10913363Bgwdr69at2rhxo4YPH64+ffooMjJSktSvXz85nU4NGjRIu3fv1oIFCzR16lSlpKRU2oEDAACcV+7H7rdt26a77rrLWj8fUgYMGKA5c+bo2Wef1cmTJzVkyBDl5eXpjjvu0PLly+Xr62u9Zu7cuRo+fLi6desmDw8P9e7dW9OmTbPag4KCtGLFCiUnJ6t9+/aqV6+exo4dyyP3AACgSpQ7EHXt2lXGmIu2OxwOTZgwQRMmTLhon5CQEM2bN++S47Rq1UobNmwob3kAAADlVmOeMgMAAKgoAhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALC9Sg9EDRs2lMPhuGBJTk6WJHXt2vWCtqFDh7rtIycnRz179pS/v79CQ0M1cuRIFRcXV3apAAAAkiSvyt7hp59+qrNnz1rru3bt0m9+8xs9+OCD1rbBgwdrwoQJ1rq/v7/189mzZ9WzZ0+Fh4dr06ZNOnTokPr37y9vb2+99NJLlV0uAABA5QeiG264wW194sSJuummm3TnnXda2/z9/RUeHl7q61esWKE9e/Zo5cqVCgsLU5s2bfTCCy9o1KhRSk1NldPprOySAQCAzVXpHKLCwkL94x//0KOPPiqHw2Ftnzt3rurVq6dbb71Vo0eP1qlTp6y2jIwMtWzZUmFhYda2hIQEuVwu7d69+6JjFRQUyOVyuS0AAABlUelXiH5uyZIlysvL08CBA61t/fr1U3R0tCIjI7Vjxw6NGjVK2dnZWrRokSQpNzfXLQxJstZzc3MvOlZaWprGjx9f+QcBAABqvCoNRG+//bYSExMVGRlpbRsyZIj1c8uWLRUREaFu3bpp//79uummmyo81ujRo5WSkmKtu1wuRUVFVXh/AADAPqosEH3zzTdauXKldeXnYmJjYyVJ+/bt00033aTw8HBt3brVrc/hw4cl6aLzjiTJx8dHPj4+V1g1AACwoyqbQzR79myFhoaqZ8+el+yXlZUlSYqIiJAkxcXFaefOnTpy5IjVJz09XYGBgWrevHlVlQsAAGysSq4QlZSUaPbs2RowYIC8vH4aYv/+/Zo3b5569OihunXraseOHRoxYoS6dOmiVq1aSZK6d++u5s2b65FHHtGkSZOUm5urMWPGKDk5mStAAACgSlRJIFq5cqVycnL06KOPum13Op1auXKlXn/9dZ08eVJRUVHq3bu3xowZY/Xx9PTU0qVLNWzYMMXFxalWrVoaMGCA2+cWAQAAVKYqCUTdu3eXMeaC7VFRUVq3bt1lXx8dHa2PPvqoKkoDAAC4AN9lBgAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbI9ABAAAbK/SA1FqaqocDofb0rRpU6v9zJkzSk5OVt26dVW7dm317t1bhw8fdttHTk6OevbsKX9/f4WGhmrkyJEqLi6u7FIBAAAkSV5VsdMWLVpo5cqVPw3i9dMwI0aM0LJly7Rw4UIFBQVp+PDheuCBB7Rx40ZJ0tmzZ9WzZ0+Fh4dr06ZNOnTokPr37y9vb2+99NJLVVEuAACwuSoJRF5eXgoPD79g+/Hjx/X2229r3rx5+vWvfy1Jmj17tpo1a6bNmzfrtttu04oVK7Rnzx6tXLlSYWFhatOmjV544QWNGjVKqampcjqdVVEyAACwsSqZQ/Tll18qMjJSjRo1UlJSknJyciRJmZmZKioqUnx8vNW3adOmatCggTIyMiRJGRkZatmypcLCwqw+CQkJcrlc2r1790XHLCgokMvlclsAAADKotIDUWxsrObMmaPly5dr5syZOnDggDp37qwTJ04oNzdXTqdTwcHBbq8JCwtTbm6uJCk3N9ctDJ1vP992MWlpaQoKCrKWqKioyj0wAABQY1X6LbPExETr51atWik2NlbR0dH65z//KT8/v8oezjJ69GilpKRY6y6Xi1AEAADKpMofuw8ODtYtt9yiffv2KTw8XIWFhcrLy3Prc/jwYWvOUXh4+AVPnZ1fL21e0nk+Pj4KDAx0WwAAAMqiygNRfn6+9u/fr4iICLVv317e3t5atWqV1Z6dna2cnBzFxcVJkuLi4rRz504dOXLE6pOenq7AwEA1b968qssFAAA2VOm3zP70pz/p3nvvVXR0tL7//nuNGzdOnp6e6tu3r4KCgjRo0CClpKQoJCREgYGBeuKJJxQXF6fbbrtNktS9e3c1b95cjzzyiCZNmqTc3FyNGTNGycnJ8vHxqexyAQAAKj8Qffvtt+rbt69+/PFH3XDDDbrjjju0efNm3XDDDZKk1157TR4eHurdu7cKCgqUkJCgN954w3q9p6enli5dqmHDhikuLk61atXSgAEDNGHChMouFQAAQFIVBKL58+dfst3X11czZszQjBkzLtonOjpaH330UWWXBgAAUCq+ywwAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANhepQeitLQ0dezYUQEBAQoNDVWvXr2UnZ3t1qdr165yOBxuy9ChQ9365OTkqGfPnvL391doaKhGjhyp4uLiyi4XAABAXpW9w3Xr1ik5OVkdO3ZUcXGxnnvuOXXv3l179uxRrVq1rH6DBw/WhAkTrHV/f3/r57Nnz6pnz54KDw/Xpk2bdOjQIfXv31/e3t566aWXKrtkAABgc5UeiJYvX+62PmfOHIWGhiozM1NdunSxtvv7+ys8PLzUfaxYsUJ79uzRypUrFRYWpjZt2uiFF17QqFGjlJqaKqfTWdllAwAAG6vyOUTHjx+XJIWEhLhtnzt3rurVq6dbb71Vo0eP1qlTp6y2jIwMtWzZUmFhYda2hIQEuVwu7d69u9RxCgoK5HK53BYAAICyqPQrRD9XUlKip59+WrfffrtuvfVWa3u/fv0UHR2tyMhI7dixQ6NGjVJ2drYWLVokScrNzXULQ5Ks9dzc3FLHSktL0/jx46voSAAAQE1WpYEoOTlZu3bt0ieffOK2fciQIdbPLVu2VEREhLp166b9+/frpptuqtBYo0ePVkpKirXucrkUFRVVscIBAICtVNkts+HDh2vp0qVas2aNbrzxxkv2jY2NlSTt27dPkhQeHq7Dhw+79Tm/frF5Rz4+PgoMDHRbAAAAyqLSA5ExRsOHD9fixYu1evVqxcTEXPY1WVlZkqSIiAhJUlxcnHbu3KkjR45YfdLT0xUYGKjmzZtXdskAAMDmKv2WWXJysubNm6cPPvhAAQEB1pyfoKAg+fn5af/+/Zo3b5569OihunXraseOHRoxYoS6dOmiVq1aSZK6d++u5s2b65FHHtGkSZOUm5urMWPGKDk5WT4+PpVdMgAAsLlKv0I0c+ZMHT9+XF27dlVERIS1LFiwQJLkdDq1cuVKde/eXU2bNtUzzzyj3r1769///re1D09PTy1dulSenp6Ki4vTww8/rP79+7t9bhEAAEBlqfQrRMaYS7ZHRUVp3bp1l91PdHS0Pvroo8oqCwAA4KL4LjMAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7XtVdAK6i/35R3RWUn39dKTiquqsAANRwBCI78K8reftLiwZXdyXl5+0vJW8lFAEAqhSByA6Co86FilM/Vncl5fPfL86FuFM/EogAAFWKQGQXwVGECgAALoJJ1QAAwPa4QnQd2nckv1rGrVPLqfrBftUyNgAAVYlAdB2pU8spP29PPb0gq1rG9/P21Mpn7iQUAQBqHALRdaR+sJ9WPnOnjp0svOpj7zuSr6cXZOnTA0d1LLT2VRnT97/5aizp4JdZ8skvUGhtn6sy7hXjowIA4LpDILrO1A/2q5YrNNVxdSpS/9VKHx9FrXnqqo1ZKfioAADllXfw+nsSuIb94++aDkQzZszQ5MmTlZubq9atW2v69Onq1KlTdZdlS9V1depgfkflHzusl5bt1Znikqs69nm+Xh56rmczBfl5X7avT94+Ra15Sge3r1JBcOMrHjvQz/vqXBmrYb/YgOtK3kFpRiep6FR1V1I+Newff9dsIFqwYIFSUlL05ptvKjY2Vq+//roSEhKUnZ2t0NDQ6i7Plqrn6lSQpOaa1vS2arlV+OPJQg19L1O9l5TtF1WkHNflVa0SLz99+eBqFdWub21jEv3V9V3e6Wp5j0v8b13tTv14Lgw98Dep3i3VXU3Z1MDPibtmA9GUKVM0ePBg/eEPf5Akvfnmm1q2bJlmzZqlP//5z9VcHa626rpVKKncV8YO5neU55mjVzzu8dNFV+XKWGPHd5qqN/TGO+9pn/kpEJXnylhlu2pXxq62i1yJ+y7vtOJfXafTRWeroSgemLhm1LtFimxT3VXY1jUZiAoLC5WZmanRo0db2zw8PBQfH6+MjIxSX1NQUKCCggJr/fjx45Ikl8tVqbWdyD8pV4E5999K3jeuTQEeUkCAoxwvuFHSjVc8bqikFyObK+9U1V418D55SHmL39YLZoZ7Q7GkD6p06Euqif/vKvH01cG7pqnYt47b9u+OnVbUyS/15K9vVv06VzeUfHfstKat3qMjux0KqFfrqo6N/9+P+6QCI53Il66Xvysn8qus5vN/W40xlbrfyzLXoO+++85IMps2bXLbPnLkSNOpU6dSXzNu3DgjiYWFhYWFhaUGLAcPHrwakcNyTV4hqojRo0crJSXFWi8pKdHRo0dVt25dORzl+Nf9ZbhcLkVFRengwYMKDAystP1ejzgXP+FcnMN5+Ann4ieci3M4Dz+51LkwxujEiROKjIy8qjVdk4GoXr168vT01OHDh922Hz58WOHh4aW+xsfHRz4+7nMOgoODq6pEBQYG2v4NfR7n4ieci3M4Dz/hXPyEc3EO5+EnFzsXQUFBV72Wa/K7zJxOp9q3b69Vq1ZZ20pKSrRq1SrFxcVVY2UAAKAmuiavEElSSkqKBgwYoA4dOqhTp056/fXXdfLkSeupMwAAgMpyzQaihx56SD/88IPGjh2r3NxctWnTRsuXL1dYWFi11uXj46Nx48ZdcHvOjjgXP+FcnMN5+Ann4ieci3M4Dz+5Fs+Fw5ir/VwbAADAteWanEMEAABwNRGIAACA7RGIAACA7RGIAACA7RGIymnGjBlq2LChfH19FRsbq61bt1Z3SRe1fv163XvvvYqMjJTD4dCSJUvc2o0xGjt2rCIiIuTn56f4+Hh9+eWXbn2OHj2qpKQkBQYGKjg4WIMGDVJ+fr5bnx07dqhz587y9fVVVFSUJk2adEEtCxcuVNOmTeXr66uWLVvqo48+KnctFZWWlqaOHTsqICBAoaGh6tWrl7Kzs936nDlzRsnJyapbt65q166t3r17X/DBoDk5OerZs6f8/f0VGhqqkSNHqri42K3P2rVr1a5dO/n4+Khx48aaM2fOBfVc7j1UlloqaubMmWrVqpX1YWhxcXH6z3/+Y7vzUJqJEyfK4XDo6aefLlcNNeF8pKamyuFwuC1Nmza13XmQpO+++04PP/yw6tatKz8/P7Vs2VLbtm2z2u3ye7Nhw4YXvCccDoeSk5Ml1dD3xFX9opDr3Pz5843T6TSzZs0yu3fvNoMHDzbBwcHm8OHD1V1aqT766CPzl7/8xSxatMhIMosXL3ZrnzhxogkKCjJLliwx27dvN7/97W9NTEyMOX36tNXn7rvvNq1btzabN282GzZsMI0bNzZ9+/a12o8fP27CwsJMUlKS2bVrl3n//feNn5+f+d///V+rz8aNG42np6eZNGmS2bNnjxkzZozx9vY2O3fuLFctFZWQkGBmz55tdu3aZbKyskyPHj1MgwYNTH5+vtVn6NChJioqyqxatcps27bN3HbbbeZXv/qV1V5cXGxuvfVWEx8fbz7//HPz0UcfmXr16pnRo0dbfb766ivj7+9vUlJSzJ49e8z06dONp6enWb58udWnLO+hy9VyJT788EOzbNky88UXX5js7Gzz3HPPGW9vb7Nr1y5bnYdf2rp1q2nYsKFp1aqVeeqpp8pcQ005H+PGjTMtWrQwhw4dspYffvjBdufh6NGjJjo62gwcONBs2bLFfPXVV+bjjz82+/bts/rY5ffmkSNH3N4P6enpRpJZs2aNMaZmvicIROXQqVMnk5ycbK2fPXvWREZGmrS0tGqsqmx+GYhKSkpMeHi4mTx5srUtLy/P+Pj4mPfff98YY8yePXuMJPPpp59aff7zn/8Yh8NhvvvuO2OMMW+88YapU6eOKSgosPqMGjXKNGnSxFr//e9/b3r27OlWT2xsrPnjH/9Y5loq05EjR4wks27dOmssb29vs3DhQqvP3r17jSSTkZFhjDkXLj08PExubq7VZ+bMmSYwMNA69meffda0aNHCbayHHnrIJCQkWOuXew+VpZbKVqdOHfP3v//dtufhxIkT5uabbzbp6enmzjvvtAKRnc7HuHHjTOvWrUtts9N5GDVqlLnjjjsu2m7n35tPPfWUuemmm0xJSUmNfU9wy6yMCgsLlZmZqfj4eGubh4eH4uPjlZGRUY2VVcyBAweUm5vrdjxBQUGKjY21jicjI0PBwcHq0KGD1Sc+Pl4eHh7asmWL1adLly5yOp1Wn4SEBGVnZ+vYsWNWn5+Pc77P+XHKUktlOn78uCQpJCREkpSZmamioiK38Zs2baoGDRq4nYuWLVu6fTBoQkKCXC6Xdu/eXabjLMt7qCy1VJazZ89q/vz5OnnypOLi4mx7HpKTk9WzZ88Larbb+fjyyy8VGRmpRo0aKSkpSTk5ObY7Dx9++KE6dOigBx98UKGhoWrbtq3+9re/We12/b1ZWFiof/zjH3r00UflcDhq7HuCQFRG//3vf3X27NkLPik7LCxMubm51VRVxZ2v+VLHk5ubq9DQULd2Ly8vhYSEuPUpbR8/H+NifX7efrlaKktJSYmefvpp3X777br11lut8Z1O5wVfBvzLGit6nC6XS6dPny7Te6gstVypnTt3qnbt2vLx8dHQoUO1ePFiNW/e3HbnQZLmz5+vzz77TGlpaRe02el8xMbGas6cOVq+fLlmzpypAwcOqHPnzjpx4oStzsNXX32lmTNn6uabb9bHH3+sYcOG6cknn9Q777zjdix2+725ZMkS5eXlaeDAgdbYNfE9cc1+dQdQFZKTk7Vr1y598skn1V1KtWnSpImysrJ0/Phx/etf/9KAAQO0bt266i7rqjt48KCeeuoppaeny9fXt7rLqVaJiYnWz61atVJsbKyio6P1z3/+U35+ftVY2dVVUlKiDh066KWXXpIktW3bVrt27dKbb76pAQMGVHN11eftt99WYmKiIiMjq7uUKsUVojKqV6+ePD09L5i5fvjwYYWHh1dTVRV3vuZLHU94eLiOHDni1l5cXKyjR4+69SltHz8f42J9ft5+uVoqw/Dhw7V06VKtWbNGN954o7U9PDxchYWFysvLu2SNFT3OwMBA+fn5lek9VJZarpTT6VTjxo3Vvn17paWlqXXr1po6dartzkNmZqaOHDmidu3aycvLS15eXlq3bp2mTZsmLy8vhYWF2ep8/FxwcLBuueUW7du3z1bvi4iICDVv3txtW7Nmzazbh3b8vfnNN99o5cqVeuyxx6xtNfU9QSAqI6fTqfbt22vVqlXWtpKSEq1atUpxcXHVWFnFxMTEKDw83O14XC6XtmzZYh1PXFyc8vLylJmZafVZvXq1SkpKFBsba/VZv369ioqKrD7p6elq0qSJ6tSpY/X5+Tjn+5wfpyy1XAljjIYPH67Fixdr9erViomJcWtv3769vL293cbPzs5WTk6O27nYuXOn2y+69PR0BQYGWr9AL3ecZXkPlaWWylZSUqKCggLbnYdu3bpp586dysrKspYOHTooKSnJ+tlO5+Pn8vPztX//fkVERNjqfXH77bdf8JEcX3zxhaKjoyXZ6/fmebNnz1ZoaKh69uxpbaux74lyTcG2ufnz5xsfHx8zZ84cs2fPHjNkyBATHBzsNov+WnLixAnz+eefm88//9xIMlOmTDGff/65+eabb4wx5x7ZDA4ONh988IHZsWOHue+++0p9fLRt27Zmy5Yt5pNPPjE333yz2+OjeXl5JiwszDzyyCNm165dZv78+cbf3/+Cx0e9vLzMK6+8Yvbu3WvGjRtX6uOjl6ulooYNG2aCgoLM2rVr3R4jPXXqlNVn6NChpkGDBmb16tVm27ZtJi4uzsTFxVnt5x8h7d69u8nKyjLLly83N9xwQ6mPkI4cOdLs3bvXzJgxo9RHSC/3HrpcLVfiz3/+s1m3bp05cOCA2bFjh/nzn/9sHA6HWbFiha3Ow8X8/CkzO52PZ555xqxdu9YcOHDAbNy40cTHx5t69eqZI0eO2Oo8bN261Xh5eZkXX3zRfPnll2bu3LnG39/f/OMf/7D62OX3pjHnnuhq0KCBGTVq1AVtNfE9QSAqp+nTp5sGDRoYp9NpOnXqZDZv3lzdJV3UmjVrjKQLlgEDBhhjzj22+fzzz5uwsDDj4+NjunXrZrKzs9328eOPP5q+ffua2rVrm8DAQPOHP/zBnDhxwq3P9u3bzR133GF8fHxM/fr1zcSJEy+o5Z///Ke55ZZbjNPpNC1atDDLli1zay9LLRVV2jmQZGbPnm31OX36tHn88cdNnTp1jL+/v7n//vvNoUOH3Pbz9ddfm8TEROPn52fq1atnnnnmGVNUVOTWZ82aNaZNmzbG6XSaRo0auY1x3uXeQ2WppaIeffRREx0dbZxOp7nhhhtMt27drDBkp/NwMb8MRHY5Hw899JCJiIgwTqfT1K9f3zz00ENun71jl/NgjDH//ve/za233mp8fHxM06ZNzVtvveXWbpffm8YY8/HHHxtJpe6zJr4nHMYYU75rSgAAADULc4gAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDt/X+NSwXlKF96KgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"initial classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "25179c37-e5c0-4f13-8205-9663ea9bea1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 699879 district pop vs 736714 target\n",
      "we'll also trim any HD with total islands' pop <= 14734\n",
      "working on HD 0\n",
      "working on HD 594\n",
      "working on HD 1212\n",
      "working on HD 1831\n",
      "working on HD 2489\n",
      "working on HD 3097\n",
      "working on HD 3770\n",
      "working on HD 4330\n",
      "working on HD 5018\n",
      "working on HD 5801\n",
      "working on HD 6489\n",
      "Out of 2184 discontig HDs 2184 had discontig HDs.\n",
      "Of these, 1972 won't be under 699879 after all minor discontigys were shed, while 212 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KpTJRqNrRyVRKjh4PaHQxHWS3nsg0xzR2key1hhAcrwDd2osf+iuQz2/ZNSaEEEI0NXdSdPEuil+8nCtKZ7FPxZNEISvM8+hiOmgMdagQTChCNZ0KZSZHj6eLqZAcPZ4cPYFCYthFMmValKunhhfuu+d+lEX7iDAOHrY3akFEp65aTeFFmRGqhcwICSGEaDS2HIpfvJw4+z5264lMr7iT58IWGUGQXYVwQMUy3jGTKMp53ryIbqYCcvQEQFFAHA84JpNPBw4Rxd2X9OS5ddsDugR3h/qGnCXKyMpl3spscm3HE7Qb43lqE8jntwRCtZBASAghRGNxVOpcnLaUd8Lm081UYNxvV6EcUBbGO2ZymEijWnQShSw3z6eQGO53TKbgWADkNu3iHiz6fEdA16Dhmi2aPvgUuidEn/DsTUZWLlOWbqoxMdXUS3OyNCaEEEK0QFWXjL7fVcQ+Fc/0ijt5PzzdGDPR8Re2q8412mXkEs8NjjTKiPSRCxR48OIOWBZ+ts24r76zN05dMW9lttfVOXXs6uatzOay3tYWtUwmu8aEEEKIhuZlR1hGVi6DnlzH9FdX8/Dyb/jnt3tIopCFYS96jHs07HWfIU0e8T4TolN7xJNUrTt8feTZypmydFPAu8w27CzyWA6rTgG5tvIWV9xRAiEhhBCiIZXshTevhCXDXS0yOL5k1NGWxQrzI7xpfoKe5LDc7FoW260nMtKe7neBxKo0XLM4550cz9wre5/w5btndOatzMap+58942/RxpZW3FECISGEEKIhlNug4FdYPhbyf4biXbBkBM7ivfz9319wHln8OzydrqYDWCliiXmBEQSNdqSxSZ3KaEeaXwUSq5t7ZW9CTBpD+yQxffApJ/yt1Gf2xt3hvqHGNRUJhIQQQogT5a4L9PZ1cLQQ9EowhULxLg4vvpS39Zm8Hf4YYZpOhTIxw3EHBcQaQZC7c3wu8UYwVFuBRLf4aHONBORpl5yCNaZhgo1AZm/OTelAkiXC57Kee+bq3JQODXJtDUWSpYUQQogTVbVZqqUrHGuWqrQQLPZ8I4+5QmlcZ0/nR3oyznEy0RytR1K0S4foMDJnX4q5Wnv5EJNG+lW9mbJ0E+CztJBfApm9CTFpzL3S9bzu3Whu7uDIPXPVksiMkBBCCFFf7qToas1SFWAPT0BTTo/hUx338iM9AThEVI0gyK22pGhwBRaPXdu3RhDkNrRPEi/d1B9rteRpf2OQ+s7e+HpeqyWixVa1ljpCtZA6QkIIIWoot0HpsR1VH08z2mRg6QI533P07bFEHs3zemr1pbD6CGR7e/UKzwO6xbFxdzEFh8rZdfAIz3z2G+B99uZEApfmriwtBRUbiARCQgghPJTshbevh6IdENUBTGGu5bC47jDkcco/vJuQo4WEaXqNUyuUiTBNr3cwNPGC7gzubW2QoMIdqKzJzuPDzfspKnMYx5q6CnRjkECogUggJIQQwlBugzeGuXaEubW3GsGQu2hgdQXKgkOF0sVU6BEM3eBI87k0Vp2Ga3npq5mXnHAQ5K0FRofoMK7t17nBAq3mFsjnt+QICSGEEP6wH3YFQ1UdyoOKMqBmEFSpNHL1OBI1G6CRo8cbu8ZKiapzR1hV7u3s3+7wbzu9L+56RtULHxaXVfD617uwHXW0+iAoUBIICSGEEP6wdIYJn7pygao6UrPWzgE9hpH2eYx0PMJuPfFYI1VXMPSrOonJjhm1JkP7MvWdwCs+u9XVAgMCL6LoftzMHYV8tHkfmTsKAz6/ucn2eSGEEKK6cptrBsjS2fN+Sxe4/k1492Yo3e/zdDtmDhBn1AWqrVlqIEqOVjBl6aZ6JTIH0gIjtYd/S3YtodP8iZIZISGEEKKqchu8dTW8frnRIsOpK/732wHS//kphW+MocTh/ePzoN6eHD2BLqaDRpsMd12gcY5Z7KBLvYOgquozc9PQLTB8LbPVt1dZc5FASAghhIDjLTL2b4b8bFcQ9PoQPvnfBgY9+E9eeP11Jm27nXhnAZajOV4fIsF0iBAqydETPNpk1FUXKBD1bV7akC0wGmuZrTnI0pgQQghRboMlV7gCoeh419b4Q7lgy+HsNdezMtxJPIfQjuURu/9UCnRMhFTZLp9kKiFXjyVHT/CrTUZ9Bdq81N0CI89W7jWAce9M86eIYmMsszUXmRESQgghDmyDgl9Ad8ChXA7ZK8nV4wBXYJOgHQ+C3CqVxo32B7nWnk6OfvzDXikoIobxjgcY55jVYDNB1fmaufGVvOxugQE1d7gF2gKjtXaa90ZmhIQQQgS3cht8MgM4PqvT3nGAo5oFp9II0Y7Pn1QoEzpQrGKY7JjBlmPtMq53pLPCnE5HrYSdKonbHH8hl4SAL0UDOsWEAxr5pYHP3NSVvOxugVF9jDXABOfW2mneGwmEhBBCBDf7YVS5DU13UqlMhB5b5nLV//FUpGK4x3Enu0jyKIaYSzzXOdJpx1Hy67krzD0PM+eK3mwrOMzCz7b5HONt5sadvFw9eHInL7t3mg3tk8Rlva0n1AKjIZfZmpssjQkhhAhqn+w2MbjwfnbriYRqOpXK90djJ1MJfzO/7LWCdB7xbK+yKyzQsoRWSwSTL0ph/upfvAZB7jHets4HmrwcYtJI7RHP1f06k9ojPuAiig25zNbcJBASQggRPPZsgB/eNm4+/kk2d76ziR2OOJY4BpOvxxgzQlUV6BYjZ6iLqZAV5nSseK/yfHnvRKZd3JO7Lu5R5+W0Cw9l4Z/PYtmk80gb0ZtXvtzpMwl5+uBT+WrmJV6XrwJJXm4orbHTvDeyNCaEECI47NkAr1/m+rvuZFXIpbz85U4AruNz5oS/4/PURJONXD2OXD2OJFMxHTUb7Tjqdex/swv4b3aBX5d02F5JYoxrCWnQk+u8zuiAa5Zl+Xd7mHZJT6/Hmyt5uSGW2ZqbBEJCCCGCQ+Hx5Sa18i6+KJ8EXMx1fM5T4a/W2BUGrp1hoceSpZNMxUYwVEAc+TRM/kvmjkJMmnZC29GbM3nZvczWWkkgJIQQom2q3ibj7LE4nZWYVt2NBjwV/irnO7O4NiQTTXNte3f/me64hUlhq+liKjQSqJWCYtpzt2NavROivVMnPKPTlpKXm5rkCAkhhGh78rPhtSGwZLjRJmPV5n2c8WFHXi+/3Ah6RoZ6BkGVysRE+328qYZyvSPdI4F6m0pmouMvHgnRDSH15IQTntFpS8nLTU1mhIQQQrQtJXvhH5dCxRHX7SUjuL/do6zYpnEpG5kQ/t8ay2BFqh1HVCRTHXcZtYGqNkwtJYrJjhn1qg1UG0tkKOcdW1Y60RmdhqoRFGwkEBJCCNG2HC4ApwNw5dZoxbuYVjgdTbuKJ83/8JgBcuugHeZF+5VGEOTmbphaRmSjVIiecEGKMUsz98reTFm6CQ08gqFAZnTaQvJyU5OlMSGEEK1fuQ1s+1x/7zKAJae9RIUyuYIKBd1MBSwI/0eNXKD3K1ON2w+HL+M6Pq/x0A3ZMLW67gnRxt8bajv6idYICjYyIySEEKJ1y8+G9yZA5VEYv5rHvy7l5R+i+YB0/h0+19j15eYOgu63T+I9Luabyj7GrrGnwl/Fadf4gD81yaVXz/mRGZ2mJ4GQEEKI1qtaPpD+xgg+zrsPiCcBGyFeMm6Ugr/ax/AeFwO4/rS7giCA30lu9MuuLeentW9Hb20kEBJCCNF6VcsHMpXsYrl5Pi9UHM8Hqsq9DHaLeS2fOM4n91i/sPe4GKdd43eS+ZFTG/WSZRdXyyI5QkIIIVqvLgNgQgZOQrzmA3HsviccN7hyhrTjY5ab53u0yfiAPzV6EAStrwVFWyczQkIIIVo1h3UAfy6fy4rwdMKq9QlTCm6z38daBpBpP4P3jo1RCo5gpozIJrnGey89hZSO0ZLz0wLJjJAQQoiWrdwGBb8e3xVWlW0fH/z3M3aQzNMV19c4XKBiyKY7AD/Sk+vs6VQoE0cJ5y7HXSe8Gyw2KqzWLvMmDV688WzuvexU2cXVQmlKKV893oJeaWkpFosFm81GTExMc1+OEEIEn/xsWDEOindDu0SYkAGWLq5jW/8Dq+7FcaiQHL0DKVq+R06QOx9ot57IaEeakQ/Ul+0cJPaEiiNqwAs39sdkgilLN7mez8u4F2/sz/AzZQmsqQXy+S0zQkIIIVqm/Gx49RI4+Bs47WDbC68PdbXM2PhPWDYaDuVhpsIIgvzJB/qJnidcIfqFG89m+JlJPmv/JFkiWHyTBEGtgeQICSGEaFnKbVCae7w2UFW2vfDi+Si7zViSqlogsaHygWIjQ6lwKsocTs/7o8J4YmRfj0Rnqf3TukkgJIQQouUo2Qv/GgtHivC+2ARUCYIO6tFEaZWYVQWT7dNZxwDgeD7Qe+HpVBDmVz7QiL5WLj/DagQyAN/+XkjmjkJAkXpyAuf5yPGR2j+tV0BLY06nk7S0NFJSUoiMjKRHjx7Mnz+fqmlGSinmzJlDUlISkZGRDB48mG3btnk8TlFREWPHjiUmJobY2FgmTpzI4cOHPcZs2bKFCy+8kIiICLp27cqCBQtqXM+KFSvo1asXERER9O3bl08++cTjuD/XIoQQooUot8HysZD/s2vmB+14PpAPkyvu51L7U1xrTzeCILcf6clIezqX2p9iO11rfZxLTkvghbEDPBKaQ0waF/RM4C9DTuMvQ3pxwSkJMsvTBgUUCD355JO89NJLLFq0iF9++YUnn3ySBQsW8PzzzxtjFixYwHPPPcfixYtZv3490dHRDBkyhPLy451wx44dy88//8yaNWtYtWoVX375JZMnTzaOl5aWcvnll9OtWzc2btzIU089RXp6Oq+88oox5ptvvmHMmDFMnDiRH374gWuuuYZrrrmGrKysgK5FCCFEC2E/DHYb6JVgCgXbXuyVOuVm3zMtz4UtAjR+qtYs1c2ffKC+nWN4/daBJ3LlohULaNfYFVdcQadOnXjttdeM+0aNGkVkZCRLly5FKUVycjL33Xcff/nLXwCw2Wx06tSJJUuWMHr0aH755Rd69+7Nd999xznnnANARkYGw4cPJycnh+TkZF566SUeeugh8vLyMJvNAMyaNYsPP/yQX3/9FYAbbriBsrIyVq1aZVzLeeedR79+/Vi8eLFf11IX2TUmhBCNrGQvFP0O8T3B0hlsOaglI9CKd1GJiVB0r6dV7R6foydwvWOusSssEINPT+Qf4/5wIt+BaIEabdfY+eefz9q1a/ntt98A+PHHH/nqq68YNmwYADt37iQvL4/Bgwcb51gsFgYOHEhmZiYAmZmZxMbGGkEQwODBgzGZTKxfv94Yc9FFFxlBEMCQIUPYunUrxcXFxpiqz+Me434ef66lOrvdTmlpqceXEEKIRlBug73fwwvnwltXwT8uxVm8l2c2lHH9gYnk6ZY6gyD3r/FdTAdZYZ7nUSW6Lr2T2vPLI0MlCBKBJUvPmjWL0tJSevXqRUhICE6nk0cffZSxY8cCkJeXB0CnTp08zuvUqZNxLC8vj8TERM+LCA2lQ4cOHmNSUlJqPIb7WFxcHHl5eXU+T13XUt3jjz/OvHnz/PhJCCGEqLdyG7x1jWsbfOWxVIVDuRx45kI+t0/nFfPTJGq2Gqc5FRSoOJJMxR7BkAMThcT4tStM02DyhSnMHt67gb8p0VoFFAi9++67vP3227zzzjucccYZbN68mXvvvZfk5GTGjRvXWNfYZGbPns2MGTOM26WlpXTtWnuCnRBCCD+V247lAR1yJUQ77ZSHJxBaXkioprBqxbwfPpcQrWbGhlNBiAZOFUKOHk8XUyFKQTlmJtjvI4sede4KiwjV+GHOECLNITUfX1ey/T1IBRQI3X///cyaNcvIr+nbty+7d+/m8ccfZ9y4cVitVgDy8/NJSjpeYyE/P59+/foBYLVaKSgo8HjcyspKioqKjPOtViv5+fkeY9y36xpT9Xhd11JdeHg44eHh/v0whBBC+K9kLywbjSq38VPqs/QM60CUM5cI+0EKVTQdKEPT8AiClIJiFU0FoXQy2ahQJrqYDpKjJ5Cjx3OISO5y3FXnjjC3Z0af7TUIysjKZd7KbHJtxzfSJFkimHtlb2mMGgQCyhE6cuQIJpPnKSEhIei6ax03JSUFq9XK2rVrjeOlpaWsX7+e1NRUAFJTUykpKWHjxo3GmHXr1qHrOgMHDjTGfPnll1RUVBhj1qxZw2mnnUZcXJwxpurzuMe4n8efaxFCCNEEym2uICg/C822lw6fTGZMyRRydFdyc7ypzKM1httcxy0MdzzBNY6/sltPJEzTqVAmimnHOMdM/uxI9ysIskSGsthHt/eMrFymLN3kEQQB5NnKmbJ0ExlZufX7nkWrEVAgdOWVV/Loo4+yevVqdu3axQcffMDf//53rr32WgA0TePee+/lr3/9Kx9//DE//fQTt9xyC8nJyVxzzTUAnH766QwdOpRJkyaxYcMGvv76a6ZNm8bo0aNJTk4G4MYbb8RsNjNx4kR+/vln/vWvf/Hss896LFvdc889ZGRk8PTTT/Prr7+Snp7O999/z7Rp0/y+FiGEEE3Afpijh4qMIohdTAd5wfwcz1dcS237lqeErUQDcolntCON3Xoiv6qTmOyYwQ661LoU1i48hGF9OvH2xIFsSrvcaxDk1BXzVmZ7Ldvovm/eymycurTkbMsC2j5/6NAh0tLS+OCDDygoKCA5OZkxY8YwZ84cY4eXUoq5c+fyyiuvUFJSwqBBg3jxxRc59dRTjccpKipi2rRprFy5EpPJxKhRo3juuedo166dMWbLli1MnTqV7777joSEBO666y5mzpzpcT0rVqzg4YcfZteuXZxyyiksWLCA4cOHG8f9uZbayPZ5IYQ4cU5dMfKJd3mh/EG6mA7WOlZXGjoQemyJLFePY6TjEXKJx0ohZUTWmQt05ZlWnhndv84cn8wdhYx59ds6r3/ZpPOkanQrE8jnt3Sfr4UEQkIIceLcAUcShawwz6szGMrXLcRrpR7B0LWOR8jzs05QkiWCr2ZeUmcg9NHmfdyzfHOdj/fs6H5c3a+zX88tWgbpPi+EEKLF+O/PrpIlucTzSMXNNY67k6Jz9VgAOplsFKoYKpWGruAgloAapubaytmws6jOcYntI+ocE8g40TpJ01UhhBANw7093uKaPXFU6sx+fwv/3rQPK4WkkMuL5mdrnKZpUK6bucMxnRfMz9LFVEisVsZd9qnsoyM76Vznclh1BYfqbqV0bkoHkiwR5NnKveYJaYDVcrwBq2ibJBASQghRf+U2KD22s+rjaVB2AOctq7j7kwI2/vQz7ThKT+Cf5sewaiXG7jCH0ghBI0Rz7TpOMhXzgvk57nTcw4vmZzlIDF/RL+AAyM2fWZwQk8bcK3szZekmNDx73bsX1eZe2VvqCbVxEggJIYSon2O1gTi4DaI6gCkEbDnse+YSjtpv5n3zG8RrpYAiXHMap+XqsdzseJAoynnR/JyRM9RRK6aMCK5zpPtMiq4esHg7HsgsztA+Sbx0U/8adYSsUkcoaEiydC0kWVoIIXzIz4Z3b4ainaBcQU55RCcOHqk0qj5XrQ3kUCGARqFqx0jHfKNBqjuBuqNWwlbVmbGOtFpngZIsEVx1VhKvfLkT8D6L85KPmkG1kcrSbUsgn98yIySEECIwJXvhH5dCxRHXbS0ElJOI8nzaa1E1gqAcPZ7xDlf5k8NEeuz+yiWe6xxzieYoBXTwGgRNH3wq3ROiPAKUs0+Ka9BZnBCTJlvkg5QEQkIIIQJzuACcjuO3lZNKTISiY9GO1Bh+d8VdbKeLz4fztS3epMGiMf0ZfmbNwGZonyQu622VWRxxwmT7vBBCiMB0GQATMsDk+l1aKQhF9zl8YdiLJFEY8NMsGnO21yDIzT2Lc3W/zqT2iJcgSNSLBEJCCCFqV24D2z7jplNXfF2ewlunv0ylMnntEwZQoCzk6PF0MxWw3Dzf72AoyRLB4pv6M/zM5Ia4eiFqJUtjQgghvHNvjT+2LZ7xq8nYG8Ks938i4kge7TjK1eERWPBcDnPnCCVqNnJVnEcwdIMjzetSWIdoM2kjTsdqiZQlLtGkJBASQghRU7kNlo6CQ7mABra9lL0ylHmFDxAJrDCn00krJkzzXBJzB0EVSiNMUySZisnVXcFQITE+K0Q/dm0f2aoumoUEQkIIIY7Lz4aSPWDt65oFsuWApQtHI5OILtvLB+Y0QMNqKjZOUQpKiSKao4RqCqUgTFNUKA2dEAqIY4bjDq+7wuKiwnh8ZF8u620lc0dhm0t8buvb8tvC9yeBkBBCCJf8bFh8ASgdRr8D41ejloxAK97FYb09ERpYTSUepygFafbxfMYAOlHMe+HphGk6SsEu1Yn7HFO8tsg4u2sMfxlyOuedHM+a7DwGPbnOYyt8UhsoaJiRlVtji39b+L7c2sr3JwUVayEFFYUQQWVrBiy7AXAVKlwYn84H++N5L+xhOplsNYZXKo3J9hmsY4Bx31ls573wdCoI4yr7I2yna43zJl2YwkMjegOuD9MpSzfVqBZ9IsURW4K2+n25tfTvT7rPCyGECNxpQ3H++W0Urg+06QfTudGUQaJWMwgCOKBi+YXuHvf9SE9G2tO51P5UjSBI0+D5MWcbQZBTV8xbme21ZYb7vnkrs3Hqrev39bb6fbm1te9PAiEhhAhG1bbEg+u3/B5vadxfPtlIep4StrrG9vg8PY4cPYEkU7HXbfE/0ZNcEmo85QtjzubKs45vid+ws8hjWaU6BeTaytmwsyjw768ZtdXvy62tfX8SCAkhRDApt0HBr64dYUuGu5KhgZU/7id96Rp6ksNd5g/JV7E1Ts07thXelSityNETjG3x1lpqBPmqC1RwyPeHaX3GtRRt9ftya2vfnyRLCyFEsPCyJZ4lI3iu60KWbcjhPXM6HTUb4Vol1bNHlYK/2a/ja85kuXk+3UwF5Ojx5OgJtW6Lnz74FKZdcorXnUSJ7SP8umx/x7UUbfX7cmtr35/MCAkhRLCwHz6+JR6FiukCxbsYuXkSH5jn0MVUaARBmuYKfmzqeBPVp8JfpTe7GO1IY7eeSAFxjHc8wDjHrBq7wmKjwlh8U3/uGXyqz+3U56Z0IMkSga/N1hqu2aRzUzo06I+hsbXV78utrX1/EggJIURb514OAxi/GuK6gy2HfSVHKVAWupgOGnWBqgZB0+zTGGJ/ktvs9xn3/yP8aU5jFzc40hjnmMV2uhhBkDlE484/nczbEwey8eHL6tw1FGLSmHulK3Ha24eqAob3cTVWbS2Jt1D79+W+PffK3q2u3o5bW/v+ZPt8LWT7vBCiVSu3wYFtsOoeOLgN2nXEOT6DOR/9xJTfp9HFVDOvx6lcvyHfZr+PtVW2xV/KRv4R/jQ6GkPsT3jdFv/ijd47xdfFWz0akwZVY5/WWJ+mrdTZ8aUlf3+BfH5LIFQLCYSEEK1WuQ3eugbyskA5XV/AfpXAFPvdvBb+FAnaoRqn7dc78DfHKN7n4hrH/sRGckj0GgTdflEKs4f3rvfluisUr8nO4/Wvd9U43lLq0wSqLVRerk1L/f4kEGogEggJIVot2z54fYgrIRpwYiIEV1+wSqURqnm+9RfoMTgw08V0kN16IqMdaeR6aY5aXYfoMP56dZ8G6RTv1FWNCtNVaYDVEsFXMy9pER+2ouUK5PNbdo0JIURbZOnMFxe8xWmfXE8SBwlBx6k0QjTlEQQ5lUaBspBkKjF2gdXWKX78+d0YckZSo8wABFKfJrVH3UGaEP6QQEgIIdqgjKxc7ng/lyTmssI8jy6mg4RoNRcA7nDcS5Y62e8t8UPOSGq0IKSt1acRrYPsGhNCiDbCUanz2v9+J+3DLKa+swmAXOK5u2Kaz3Pmhr0F4NeWeGjcIKSt1acRrYPMCAkhRBvw+CfZvPq/nVTfZZ5EIS+EPVtjvFOZCNF0upgKWWFO53pHOjc40igj0msA5NaYQYi7Pk2erdxrHyt3jlBrqU8jWgeZERJCiNamWp+wxz/J5uUvXUGQlULacwRwBUHvm+eQdKxGELgSpQFCNJ1K5foIcAdDQK1BUGMXyWtr9WlE6yCBkBBCtCbH2mSoJcPZuOUn3t+Ywytf7gRcgc+/zPN50/wEPclhhXmeEQQpBbl6HKPs88jRXQ1RQ48FQ3YVysFa2mSAKxBpiiBkaJ8kXrqpP1aL58yT1RLR6rbOi9ZBlsaEEKI1sR/mSHEeUWV7SXhvJNMcaSjiSaLQSHhGd+2wKiSGjqqEg6o99zmmsIsk8ojneocrgbqjVsJ2lcSDjtv4nc4+Z4Oiw0N4atRZWCLNfLR5X6PXixnaJ4nLeltbZH2alq6l1vVpyaSOUC2kjpAQoqXJyMrlkaVrWHYs6NmtJzK94k4Whr1o3HbXAGrPETpRxGEia2yDt1JINEcpoIPPAEgDrjjTyrA+Scxf/UuLrCAsjmvJlZ6bmhRUbCASCAkhml25zdUs1dLZo+BgEoXGtni3QAoh+nLNWcnERIXRrUMUN6d2Z92v+UxZuqlG8nJrrfTcVmVk5crrVIUUVBRCiLbAnQ9UdoBNFy9l7f5Qj9/2Q3F6DJ9ececJBUEAN5x7klEnyKkr5q3M9rqDS+H6kJ23MpvLeltl+aUZyet0YiRZWgghWqpj+UBa8S4S3hvJB198B3BsNijd6BjvtjDsRZKo2UjVHxo1d4UFUulZNB9/X6clX+/EWb2+gpBASAghWqqMvSYGFz7Abj3RaHvRX/uNFeZ0o3N8jp7ASHu6x5hAgyFfW9Ol0nPr4O/Pf/7qXxj05DoysnIb+YpaFwmEhBCipThWH8hRqfPql78z418/sp94RjvSyNHj6WYq4P1wzyDoesdcNqlTjcrQ7mDIGkAw5GtrulR6bh0C+fnn2cqZsnSTBENVSI6QEEK0BMfygYoP7OeK0lnsU565PqGa7nE7T8VxvWOukROUeyxgWm6e77NPmDdpI05n/AUpXnNHpNJz61DX61SVO2co/eOfaR8RxsHD9qDfZi8zQkII0dzKbXBwO8UH9hNn38c7YceXt85kO++Z52LVPPOB3FWhq8olnhscaT77hHmT0D7c5wegVHpuHWp7nbxRQF6pnbH/WM89yzcz5tVvg3rJTAIhIYRoLuU2KPjVtTPsvQmMt91uLG+tMM/jKu0r3g9Pp7PJlYyco8cb+UBdTIVe84HyiPc7CIK6l1WautKzU1dk7ijko837yNxRKMm9fvL1OvkrmJfMpI5QLaSOkBCiUZTboDQXPp4Gh3JdyxW2HHbricxz3MxfzW+QbPLciZWrxzLSMZ/calWkd+uJ3OBIq1Ew0R8dosP47qHL/JrRaYqKxVIQ8MQ5dcWSr3cyf/UvAZ/rXur8auYlrX6WL5DPb5kREkKIpnQsF4i3r4NDuWDLYV/xUXL0BLqZCnglfCEdtRKPUwp0ixEEwfF8oN16YkD5QNVd26+z3x94ISaN1B7xXN2vM6k94hslCJqydFONbeDBPFNRHyEmjfEXpJBkifBrmayqYC2HIIGQEEI0JfthKDsAtr3YjlaQo8fTxVRICJVUKhOhmk5YtcRoB2E1HqY++UDVDe5trdd5Da2ugoDgKggoy2T+CTRnqLpgK4cggZAQQjSmY1vi4Vj+y8EIPv3DaxwITcLiyEdDkafHkWQqqbEzbLZjwrF8oIMNkg/k5q14YnOSwo0N70RyhoKtHIJsnxdCiMbiXgYrO8AXqW8we22x8YF/FlN4r0oitDd3hK7ibsc0njMvMuoD1TcfyK0l7vaSwo2NY2ifJC7rbTVyuxKiw7lvxY/kl0o5hKpkRkgIIRpLaa4rD6h4FymrbjBmhpIo5AXzszWWwNwK9BgjZ+g58yLudkw74Xwgt8ba7XUipHBj46ma23XBKQmkXyXlEKqTQEgIIRqSeyms3AYfT0MB+0k4tiU+nUHaFqNFRvU9u5XKRL4eQ6KpFFAewdA0xzSv+UAdosKwRNbMIfImPtrM/91/cbMEQbVti3cXBPT18dvSlvJas6Yuh9AayNKYEEI0lCpLYYx6DcoOoNly0PUEcomli6mQpeFPGMO1Y5/8DhWCCUWoplOhzEYCdY4eT46eQCEx7CLZaz7QYyP7AnDH0k11Xl5hmYONu4uN7vJNpa5t8e7k3ilLN6GBx7JNMM9UNJbqS2ZSWVoIIUTDcO8IK96F+vdExpVMMpKdO2qlNYerEHL0eIbbH69SKPEgoJGjx1NAHOMdD/jcGXbr+d0Y2ieJoX2SmHBBd78usanzbPzdFi8zFU2rscshtCYyIySEEA3F0hnGr+bIK0OJKt7FI/qz/LViLC+Zn62xI6xCaYy3z2QnSUbyc9VeYfc7JlNAh1p3hXWJO37sst5WXv96V52X2JR5NnVti9dwbYu/rLeVEJMmMxWiWciMkBBC1FeVrfHg+uD/+7dlXFr4gNEt/tXwhTWCoHw9ljBN8YT5Hx55MVVrA+2gS51b4zu0Czf+3hLzbOqzLV5mKkRTk0BICCEC5e4R9tY18PoQsOWQkZVLn7kZvPv5eqI5ilmr9Hl6BaFGoLTcPB9rlfpAgdQGSqwSCLXEBqmyLV60BhIICSFEINwJ0UtHQl4W2PZStOgS5i1dQ2zFAd4zz+OT8NkkajaP0yqVicn26TXygE5oS3y1mKal5dnItnjRGkiOkBBC+Cs/G/b/4EqILt2HkxBCgA4V+aw2z0LXQkiokhStlGtnmLt1xkNhb3sUSMzRE3jAMbneLTIOHrbXuK8l5dm4l+vybFLAT7RcMiMkhBD+yM+GxRfAR3fCoPsoCE0iBCeVyvU22sFU5hEEVSjYqrqwW09k1LEdYe6aQHcdK5BYQCz51D8I2HWwzOv9LSXPpiUu1wlRnQRCQgjhj5I9oFxJz2rlXSw4fAW79cQaidAAlUrjOvsjXO9I5wZHGj/S06Nb/E6ST7hhKsCyDXtafCPSlrZcJ0R1mlLVa5sKt9LSUiwWCzabjZiYmOa+HCFEUyvZC4cLoMsA1+1fV6OW3+gq+qfgU+cAhoZurHFagbJwtf2v5FbrCWalkDIiTyj4qW7ZpPOavEBifTh11SKW60RwCOTzW2aEhBDCm5K98OJAeP1yyPkOp64Y+G8zt5XfZ+T+eAuCABI1GyvM8xqsW3xtvt5+oMXPCkHLWa4TojoJhIQQorpyG+T/DJV20CtxvjaEPz/8HPmHHCjA6eUUd66Q+88upoOsMM/z2BofiA7R/vUPW/T5DgY9uc6o0Cz8U1vvMxFcZNeYEEK4ldtcHeM/ngZlB9g48FnO/OYuwnDyr7B0nq+4mnvDPjB6hLkpBY/bb+AW81q6mQqMXWIdtRKiORrwZTw0/HTGnd+dPz71uc8dV1W521VIzo1/6up91lxk+bB5SI5QLSRHSIgg4q4PdCjX1f7BlsNuPZFHHDfzspfq0ErBARVNR60MTXPdvt8+ibvMH1FKFB2wcYBYbnY8FPBy2D9vPZcLT+to9OkC6gyG3FvRv5p5iXx41sL9M63+83T/xJormGypwVlrJTlCQggRKHfDVFsO+0uOkqMn0M1UwDzzm5ThuePJFfRM5lLHs/zFPsnIGXoq/FWecVzDjY6HGeV4pF5BEMD6Xa7lNF87rrzx1q5CeKqr9xm4ep819TKZv41pReOQQEgIEbyq9gqzdOb+do+yW0+ks1ZICJXkq1i6mA5i0Y54nOYEth3rBfZvLuY2uyuBWkfjJ3pwiKgTSozWFUb+iiXSzP/dfzHTLu7p17nSrsK3+vQ+a2wtNTgLJpIjJIQITu6lsLIDOG9Zxai3d7E5R+Mr0lhhTqeLqWaS8/KKi7gu9EtCNXgvPJ3r7On8SE/WMoBb7feRQyLb6XrCl7b8u728+MUO43aSJYLRf/DvcaVdhW8tsfdZIMFZayiT0BrJjJAQIri4Z4HcS2HFu8hZeAn5OTuxUkg0R70WSQRIDfmV2+33UaFMhGk674Wn05ftAHzBgAYJggCKyhwet/Ns5Sz8bBuxUWEtqrt8a9MSe5/5G3R9lp3XyFcSvCQQEkIEh3Ib7P0O3rwSlgwHFPdF/dVoffGhOY2PzGmsDn8Qq1bscepBvb2RMzTH/E/usE+nQpmwE8ZBYhv90hWeLSqkXUX9uHuftaRg0t+g64PN+2R5rJFIICSEaPvKbfDWNa4AKC8Linexd+ElfLOjmLsd06hUJjqZSuhkKiFcqzROK1AW8nULCaZDgPIIhibZp3OZ/SlySWiQS6yrbpACSo5UcO/gU6VdRT21pN5n7jpGebajtI+oO0ulqKxCEuEbieQICSHarnKbawkMXMtgTteSU4XS6Krl8555DmZNr7EU5lAhFKg4rnfMBWC5ef6xbvHx5OgJFBLDRk4/4SrRDw7rRceYCKwxEeSVljP9X5vrPKd7QhRfzbxE6s3Uk3snXvWt6tYm3Krubau8PyQRvnFIICSEaJvKbfDW1a4AaMKnMCGDvIV/xMpBwjSFUtDZVOz11IPKwp2Ou41eYaMdaSw3z6eQGB5wTCafDiccBN1+UQqT/9jDuJ25w78K1IntI4x2FaJ+hvZJ4rLe1mYJJn3VMfKHJMI3joCWxrp3746maTW+pk6dCsCf/vSnGsfuuOMOj8fYs2cPI0aMICoqisTERO6//34qKys9xnzxxRf079+f8PBwevbsyZIlS2pcywsvvED37t2JiIhg4MCBbNiwweN4eXk5U6dOJT4+nnbt2jFq1Cjy8/MD+XaFEK1ZaS4qPxtsOZS9dCmXPv0F15bPJVePA6hRHfqAsnCTfRY5egLJpiKeNy8yeoXlEm90i99+bNt8fZk0VxA0e3hvj/tbYv5KW9Ycvc9q2ypfG3ntG1dAgdB3331Hbm6u8bVmzRoArr/+emPMpEmTPMYsWLDAOOZ0OhkxYgQOh4NvvvmGN998kyVLljBnzhxjzM6dOxkxYgQXX3wxmzdv5t577+W2227j008/Ncb861//YsaMGcydO5dNmzZx1llnMWTIEAoKCowx06dPZ+XKlaxYsYL/+7//Y//+/YwcOTLwn5AQolX6fNdR8pztAYguz+NN5pCkFRLiY0eYXYWyQ3XmesdcI4F6uXm+0SvsROoC3XzeSdyS2o20Eafz6/xhNYIgaFn5K6Jx1LVV3ht57RvfCbXYuPfee1m1ahXbtm1D0zT+9Kc/0a9fP5555hmv4//zn/9wxRVXsH//fjp16gTA4sWLmTlzJgcOHMBsNjNz5kxWr15NVlaWcd7o0aMpKSkhIyMDgIEDB/KHP/yBRYsWAaDrOl27duWuu+5i1qxZ2Gw2OnbsyDvvvMN1110HwK+//srpp59OZmYm5513nl/fn7TYEKIVcecDWTobyw9WCllhnkcX08FaT61QEKbBbj2R0Y40AGMpbJxj1gnNACUF2PZCWi20XR9t3sc9yzcHdI689vUTyOd3vXOEHA4HS5cuZcaMGWhV5pjffvttli5ditVq5corryQtLY2oKNebSGZmJn379jWCIIAhQ4YwZcoUfv75Z84++2wyMzMZPHiwx3MNGTKEe++913jejRs3Mnv2bOO4yWRi8ODBZGZmArBx40YqKio8HqdXr16cdNJJtQZCdrsdu91u3C4tLa3nT0cI0aSqFUec+9FWVyE64pnquJt/h6fXSIiuVFBMDB21UsI0VwK1exboBkcaNzjSKCPyhHOB0kYE9pt8c+aviMblb45P2ojTSWgfLq99E6l3IPThhx9SUlLC+PHjjftuvPFGunXrRnJyMlu2bGHmzJls3bqV999/H4C8vDyPIAgwbufl5dU6prS0lKNHj1JcXIzT6fQ65tdffzUew2w2ExsbW2OM+3m8efzxx5k3b57/PwQhRMtQpTjiwUWDMZU9BMSTRCEvmJ/12jD1dvt9ZNPdmDEK0xQVSqOUqAYJgNzios0BnyPJ0G2TOw8sz1buNU/I3Th3/AUpEvw0oXrXEXrttdcYNmwYycnJxn2TJ09myJAh9O3bl7Fjx/LWW2/xwQcfsGPHjloeqeWYPXs2NpvN+Nq7d29zX5IQwpdqfcI++8Pr7NYT6eTMY4U5nUHaFp+tMjQN5pmXAHC9Yy45egJ2FcpvqiuTHTMaLAgC2fIcbNz1gT7avI/MHYUeRRAlD6xlqteM0O7du/nss8+MmR5fBg4cCMD27dvp0aMHVqu1xu4u904uq9Vq/Fl9d1d+fj4xMTFERkYSEhJCSEiI1zFVH8PhcFBSUuIxK1R1jDfh4eGEh4fX+j0JIVqAY0thquwAmy5eSvr/lfDTvlKSqvQJWxr+hMcp+/UOPFExmtlhy0gyFdPFVMgKczrXOdK5zjGXaI5S0ADb4quTLc/Bw5/8rpZQx0h4qteM0BtvvEFiYiIjRoyoddzmzZsBSEpyvbCpqan89NNPHru71qxZQ0xMDL179zbGrF271uNx1qxZQ2pqKgBms5kBAwZ4jNF1nbVr1xpjBgwYQFhYmMeYrVu3smfPHmOMEKIVqtInrKw4D614FwnvjeTgvtr7hOXoCYxyzONjNYiRjkfI0V3VoDtqNtpxlDzi2RHgtnhzqIm4KN/VoGXLc3BxJ+hX3xWWZytnytJNZGTlGvcN7ZPEVzMvYdmk83h2dD+WTTqPr2ZeIkFQMwl4RkjXdd544w3GjRtHaOjx03fs2ME777zD8OHDiY+PZ8uWLUyfPp2LLrqIM888E4DLL7+c3r17c/PNN7NgwQLy8vJ4+OGHmTp1qjETc8cdd7Bo0SIeeOABJkyYwLp163j33XdZvXq18VwzZsxg3LhxnHPOOZx77rk888wzlJWVceuttwJgsViYOHEiM2bMoEOHDsTExHDXXXeRmprq944xIUQLUm6DfT/Ap7Oh4giLTnqGtwsfMCo+f2R+CDQNC2UeLTIAKpWJqVWKI+YSz/WOuawwz6OQGPKpX6DiqNS59fxuvPLlTgCPnA9Z5ggutdUHcveJm7cym8t6W41/D5IH1nIEPCP02WefsWfPHiZMmOBxv9ls5rPPPuPyyy+nV69e3HfffYwaNYqVK1caY0JCQli1ahUhISGkpqZy0003ccstt/DII48YY1JSUli9ejVr1qzhrLPO4umnn+Yf//gHQ4YMMcbccMMN/O1vf2POnDn069ePzZs3k5GR4ZFAvXDhQq644gpGjRrFRRddhNVqrXMpTwjRApXshVcGwz+vhoJsKN7FlT9MBmCe42YqFCSaSknUbEYQlKPHM9Kezh49gVBN57kqxRHBFQxd55jLTY4H670UpgEf/5jLCzeeLb2/glxd9YEUkGsrl15hLdQJ1RFq66SOkBDNrNwGbwyH/ON1xZRyJTvn6nF01IoJrTbhkqfHca3jEXKP7Rpzzxrt1hO5wZFGHg37W/iySedxbkoH2e4exPytD/Ts6H5c3a9z41+QaJo6QkII0ejsh6G8xOMuTXMFQ0k++oRVEmL8PZd4jz5hZUQ2+CUWHCqXZY4g529CvCTOt0z13j4vhBCNztKZ/567hBzdM8io3iesSI8y+oR1MR1kuXm+1z5hDb0jDOTDTUifuNZOAiEhRMtRshdyNgKuZOTzHlvD5JUFXO9IJ1+3+DztCFGN1ifMF/lwE27NWR+otrpFwj+yNCaEaH752bBvI2TMhEo7S057ifQfogH4ExtpRzkJ2iGvpyqFMQs02pHW6EthVcmusLbJqauAc76aoz6Q9KVrGJIsXQtJlhaiCezZAK9fDigUJjR0KpSJ6+zpnEIOT4W/AtRcDqvKnUDtTogGGrRNRnXx0WYevbaPfNi0QScaXNQniKrvdU5ZuqnGln33MwX7rsVAPr8lEKqFBEJCNKKSvZD3E3z6IKp4JxqugMaJRqimcCrX2n31AEgpKFZRdDAdqXH/VtWF6x3pjRYAAXSIDuPb2YMxh0pmQVvTWoILp64Y9OQ6n1v23T3Lvpp5SdDOWAby+S3/k4UQTW/PBlh0Diwfg16yxwiCNA1CUCgFIZr3IOh++2RGOJ70SKBWCsoJY5rjrgYLgrzlemjAY9f2lSCoDaqrKCK4iiK2hBwcqVvUsOR/sxCi6ZTbYO938NaVUOl6IzcpJ5XKZGyL17wEQJXKRIFuYYL9Pt7jT8eqQ6eTo8djV6HsUFautP+V7XRtkMucPvhUKZIYZFpTcOFvI19p+OsfSZYWQjSNkr2wbDSUHUA5KzxmgUI13fi7d4pZjtv4nAHGPa7q0Om04yj5DdQs1b2kMO2Snky7pKcUSQwirSm4kLpFDUsCISFE4yvZC29dDUU7ADigW4jTSgnTlM9ZIPDMGXo5fCHX2dP5kZ7G8YauEg2eO8GkSGLwaE3BhbtuUZ6t3OtSnjugl9IO/pGlMSFE4ynZC9++DK8PMYIggESTjUMqispaZoGq5gxVKo0wTee98HT6sr1RLjWpAZe+pLZL69OaiiI2Z92itkhmhIQQjeObl+C/s8Hr76zQwVTm81Sn0gipMlsUolzBkJ0wDhLb4Jc6ffApTLvklAb54JDaLq2TO7iYsnSTa9m2yrGWGFw0R92itkq2z9eirW6fP+Ko9HnMpGlEhIU0+tijDifKxwekhkakuX5jyyuc6LX8k44yhzb72MiwELRj0yD2SmetswWBjI0IDcF07E3aUalTqesNMjY8NMR48/drbGkO/O8ZKr5/g4oqv2tVzwEyU0Go5nqsChVCBaEeYyqVycgdCqWCGY4pbOI0cknw+fyBssaEk37VGQztk4RTV9grnT7HhppMxm4xX2PXZOdxz/IffT7Gs6PP4rLeVp+Pq+uK8lquIcSkER7q+veulOJoRcOMbar/9y3pPaLq/9mqWlsg21R1i1obqSPUQNpqINR91mqfxy4+rSNv3Hqucfv0tAyfb6ADUzrwr9tTjdv956+hqMzhdeyZXSx8PG2QcfuCJ9axr+So17GnJLZjzYw/Grcv+/v/sa3gsNexnWMj+XrWJcbtqxZ9xZYcm9exHaLNbEq7zLh9w8uZrPexAyQyLIRf5g81bt/6xgY+33rA61iAXU+MMP5+59sb+eSnPJ9jsx8ZYrwJ3/fuj/x7U47PsRsfHkx8u3AA0j7M4p/f7vY59n8PXEzXDq6E4cc++YVXvvzd59j/Tr+IUzu1B2Dhmt94du02n2M/mnoBZ3WNBeDl/9vB4//51efYZWNOJnXln6CynDcrL2Nu5a0+x74etoBLQjYDsKLyIu6vvMPn2G7sZzfJPo/XV/qVvRl/QQoAmTsKGfPqtz7Hzh7Wi9v/2AOAH/eWcPULXzfINUy+6GQeHH46AHuLjnDhgs99jr35vG7Mv6YPAIWH7Qz462c+x47q34Wn/3wW4ApAes/51OfY4X2tvDj2eCJ6MLxHVP0/W50EF62fdJ8XQjSPrRnGtviG/NhojCAIMGbFhKgqxKRJonwQkRmhWrTVGaFgmPb2RZbGAh9b59JYeSm8PhyKfyccB5oGJpTX5a6qvC2NHdBjiNUOUUko0x23s4nTOXwC2+ItEaHYyo//u+wUE86Dw3sZy1NhISbCQmpf7nKra2ls1Zb9PPDeT3Ve04Lr+nLFmccDO1ka8z62uZbGRNsgM0KiVoG8ATTW2KpvTA05tuobaWsY6/6gauix5lATZj83hZ7Q2D0bYMsKKPnFYwrIqTTCNCdhOH1ODVWNFd1jraZi7rXfwWZObZBcoBfHDsBk0vxa4ggxaX7/G/Y2tmtctF/ndo2L9vk8pgCuQdMaZyzIe4QILhIICSHqZ88GeP0yr4eq7vjypWolaXD9/Xdl5Sv6NUhxRJMGtqMVDD+zaRJcpbaLEK2T1BESQgSu3AY5Gzzuqr4a6KtAYvUxSkGF0tiqujDB8UCD9QrTFUx9ZxMZWbkN8nh1kdouwUPqRLUtkiNUi7aaIyTECSm3wdJRUHaAHzuN4sxfnvaY1altFqj6GPe7z1T7nfyP/g3eNb45unC3tu3XIjDy+rYOsn2+gUggJIQXtn2wZDgU72K3nshbjkt5OHxZrQGQrlxLVW7ud51KTNxuv5t1nOv9xAaybNJ5TboLSLZft00ZWblMWbqpxtKn+5WVprwtRyCf37I0JoSoXcleyNkIuD7g/5dnJjX3PnbriXQzFXCr+b986zy11ocwaa4EanAFQQp4zX4pF9mfafQgCJq+UaZ7+/XV/TqT2iNegqA2wKkr5q3M9pr/5b5v3spsWSZrhSRZWgjh2+Z3YeVUUDpfX7iUW9foOHSAeL529OKk8AK6mArpYiqscWr1ZbIQTeFUGvtUHHc57uZHag+eGlJLaJQpWrcNO4s8lsOqU0CurZwNO4ukBlErIzNCQgjvslfBh5PA6QC9knO/uJHTdVfD08dZzJjwL2sshyl1fNnLnQhd9f4dKokbHOlNGgTFR5tlp5Y4Yf7OKvoaJwnWLZfMCAkhaiq3oT6dBbjyH5TC6P7+ZUVfLgn7scb2d4BSFc4bjqHcE/4RmnY8GFLA/faJ/JfUBk+IrsvV/ZJlaUqcMH9nFb2NkwTrlk1mhIQQLiV7XcURt2bw+Zbf2WdzGEGQO6AJ03QuNXsGQUqBTYVzQG+PxWTnWnMmf7WPMWaB9qoOXGtP599c2uRBEFCjyakQ9eGuE+UrpNZwBTfVZx/dCdbVl9XybOVMWdp05R2EbzIjJISA/Gx45U/gtKOApeX3kc3DLDfPp5upwOu2eE2D/Xosbzku40MuQgNj/C3mtcyz38huOvE9ZzRLAATeP5iEqA93nagpSze5fkGocsxXnai6Eqw1XAnWl/W2yqxlM5IZISGCXbkN3h0PTjvgenP+R/jT9GYXzzuu9lkbSClIc0xkMdeSRzy5xDPakcZuPZFCYvg3l/A5f2iWIEg79uWtgKHkaoj6GtoniZdu6o/V4rn8ZbVEeN06H0iCtWg+MiMkRDArt8HB7VBZ5nG3prmCIfffq3MHR4vDF3KdPZ0f6QlALvHc4EijjMgmC4DiosIIDzWRV2o37rP6yL+QXA1xoob2SeKy3la/6kSdaIK1aBoSCAkRjEr2QtHvsG4+quwAmwcuJPHTO+isHd8G76tAYvWcoffC0xlpT+enY8FQHk27dfjxkX39+mDyVQzPnashxfCEv9x1oupyIgnWounI0pgQwSY/G144F/55LeUH96AV76JDxlTutN/DAb2d11OUgpX2P7BbT6yRKK1j4iCxTfs94Fr6uv2iFIb2SaqzgKEUwxPNob4J1qJpSSAkRLAot8He7+Hdm1EVR0A5CTlaQK4eRzdTAa+bF5CgHfZ5+ofqIiMHqGqNoBn2SeSS0GiXHRnm/W1KAa98udOvXTeSqyGagzTibR0kEBIiGJTb4K1r4I1h6EU7q9QGUnTUSnAqjXjTYZ/LYe6cod7sMoKhHBXPRPu9rObCRr742mdp/JnJkVwN0VwCTbAWTU9yhIRoy7Z/Dru/hi7nQNkB0B2YgEplIlTTUQpCtZpBhFJQoiKJMx017nMHQ7fa72vShOijFb6DHH/bGkiuhmhOgSRYi6YngZAQbVX2Knh3rOvvmonvzn2epMy5dDEdNIIgXzNAhao9Ex3384L5WaOPmCsfCHJIbPKE6LrUNZPjztXIs5V7nV/ScP2GLrkaorH4m2Atmp4sjQnRFu3ZAB9NPX5b6fT/dhpzHeM4pIcBvrfFl+hRJJgO8Zx5EVMd95Cjx2NXIexWCYy0p7Odrk30TUCYn78x1zWTE2LSSBtxus8gCCRXQ4hgJTNCQrQl+dmw8yv4dCYo3eNQiKaM2kDeuGeIojlqJFA/Z17EFMc9HCWCfDo0eXHECl15JGZX5+9MTkZWLvNX/+L1mK+aQ0KI4CCBkBBtRX42LL7AIwCqvvzlaymsUmmEaspIoE6ghFw9jkJi2EVys7XIgNqDIKh7JsdX/SC3tBESBAkRzGRpTIi2Yt8mIwhSeNb68UYpKFauACdUU1QqkzG+AjO3O+5lnGNWswZBVVWPdfzZdVNb/SBwBVPzV0v9ICGCmcwICdHaldvAfhgST4dj7SDdTSFrS4hWwF/sU5hnftNIoK5UJnaqRKY4pjdpLpA/dAVpI04noX2437tuAqkfJImsoik5dSW7yFoICYSEaI3KbXBgG1SUwbr5rq3x41fDbWtw/mMwIfgOgNxMGrwS/ncm22cwz/wmHbUStqskbnPc36gFEk9EQvtwru7X2e/xUj9ItETS865lkUBIiNamZC/881oo3A6aCdp1hEN5HH11GE9r47hTjyROO1prs1S3EE3xSvjfmWifwV46UdAMCdGBCLTOj9QPEi2N9LxreSQQEqI1KbfBstFQuM11WznRDxdQoGKxHt7DQ2o+mpfMP3eeUNU+Ye77KwhlK90adBYoNiqMW87rRvuIUHYXHeGjzfs5XF5ZR41o3+pb56e56gfJsofwpq6edxquSumX9ba2mn8vbeHfugRCQrQW5TY4uN31ZxUmpdORklrzgdwBUNUEaicauSqOWx0zGywIuiW1G8P6JNV4MxzUM4EpSzcZuUuBOJE6P+5eT96eu7HqB8myR3CrLTBoazlrbeXfuuwaE6I1KLfB0lHw7wlk9v8b+6sFLiFazSBIKfjAcR6VVWaD3PfraEy238twx4IGTYoe1ifJa/d3d78lS1RYnY9R/fs40Z5MTdnryb3sUf3Dzr3s4U+DWNF6ZWTlMujJdYx59VvuWb6ZMa9+y6An1xmve1vKWWtL/9ZlRkiI1sB+2JUQXbyL5LV3McVxNy+Yn6OL6aDPU4pUFAv0sSyxD+ff4XMIPRZgvGAfzof8scF3hSXVscR0WW8r6R9nAxU+x8RHm/lq5iVs3lvSoFPtTdHrqS0uewj/+ZP701Zy1trav3WZERKiJSq3gW3f8duWzjzX9Rl264l0MxWwyPw8Sysv9XqqUnBAb0e86QjLzfMpII5R9keoVHCEMN5maIMHQRp1LzFt2FlEXmntv+kWljnYvLeE1B7xXN2vs9fZpfpy93pq6Md1C2TZQ7QtdQUG4AoMBnSLI8kSga9/eRp1/0LRErS1f+sSCAnR0pTshTevhCXDwZYDwKOrs/n7hiPMc9xMpTLR1XSAWeZ/eT1d0yBOO0KuHks3UwHLzfPJJ45r7Y8w2P50g2+NT/JziaktLQt409a/P+Gbv4HBxt3FzL2yN0CNYKg19bxra//WJRASoiUpt8HysZD/MxTvgiUj+O/X3/Pq/3ZyFtt5OXwhoZpnDzGlMPKA3EI1nY5aKbl6LIXEUEYkP9GzwYOgaRf35KuZl/iVZ9NWlgV8aevfn/AtkMBgaJ8kXrjxbOKiPfPlGiNnrbG0tX/rEggJ0ZLYD4PdBnolmEKheBenfTqGy7XveC88nTBNr9Ey46CKYbL9PnL040GOUuAglNsd0xu1TcYFPRNq/Pbq1BWZOwr5aPM+MncUGu0rBnSLo0O02edjBbos4Ot5mot7q35rX/YQgQskMHA3AC4qO54r1yHa3Kp63rW1f+uSLC1ES2LpjPOWVVS8NpyIw3uoUCa6mQp4JXwh4Ln9vVhFU0koiSYbc83/ZOqxBOqOWgl7VEKjtsmoXn/HvWX4s+w8Pti8z+NNPskSwRVnJvHvTfsoKnP4fDzwf1mgJW7bbY6t+qJl8LdeVXGZnanv/FBjTHGZg6nvbOIlU+uYEWpr/9Y1pXy1ZBSlpaVYLBZsNhsxMTHNfTkiCLg/4LHtY7l5Pt1MBTXG7NU78oBjEjtJQgNj3G49kWmOaRwhokEqRF/SqyOf/3oAqFn7RwNjGt9bUBKoQIIYX7tz3G+5zb280BKDNNH43P8uwXtg8MKN/Zm/2vf/E3ew9NXMS1pNANGS/60H8vktgVAtJBASTcWpKxat287Cz34z7rtM+45Xj80EuVUoE9fZ0/mRnsZ9SRSy3DyfQmIadBls2aTzsB11MOv9nyg54rnlPTYqjCdG9gXwGpQEokN0GN/OHow59PhKva+idE5dMejJdS3+w6QtVNsVgastMLBEmhnz6rd1PsaySee1imKKbi3133ogn9+yNCZEU3N3i7e4modmZOWS/vHP5JXasVJIGZGczH5eND/rcVqF0gjTdJ4zL2K0I41cXG+WucRzgyONMiIbJAiquuy1JjuvRhAEYDtSwR1LNxEbFXZCQRBAUVkFG3cXk9oj/lhAuI03vt5FyVHP5TX3h0lrqMzr3qovgktt9ao+2ryv7geg9ey0cmsL/9YlEBKiKbkrRJcdwHnLKhZtPMrCz1x9w9wzO0cx00PbR5imqFAm7nTcw8Nhb9PNVGDkDC03z+cGRxp5x4Ih958nqur6Prhqn3jjDn68BUn1UXConIysXK+zT3C8KN2EC7r7/XhCNAdfgUFb22nVlkggJERTqlIhOvfZS1le/jAQbwRB7mDHHQS5l8GyHCd7HC8lijIiG/zyrFXW9zN3FJ5Q3k8gdh08wjOf/eZzdsldrfYDP3+rbokfJi11CUE0jeZqACzqJoGQEI2t6lKYpTNfpL5Byqob6GbKZ4U5nVkVk3g07HW6mQrYq3ekhCgsqoxpjrvZciwXKJd4RjvSWG6eTylRTHbMaPAt8dMu7sn0y041PpybalalQ1QYyzbsqXOJTeFaRusQbaa4zNGqPkxaclKpaBptbadVWyJ1hIRoTO6lsGNVop264p7/HGC0I40cPZ4upkKWhj9h7Pr6s2MONzrS+LNjrhEEublzgW50PNzghRGhZk2gpppVuSW1e52tN6q6pl8y0Hoq87al5pTixDRlA2DhP5kREqIxVVkKY8kI5lqewHZUJwpqVIieXnGnkQDta7anoXKBqvI1i1LXVH5DuP2iFFI6Rgd0zmW9rZyb0qHGDIu1Bc6wtLXmlOLENUUDYBEYCYSEaEyWzjB+NSwZAcW7mFR4N9nanTwXtgirVuwxdGHYix67wZqKwvssSm1T+ScqLiqUR6/py/Azk8ncUej3ee5qtSEmrVV8mATSnLK177wR/msLO63qqyXmykkgJEQjcuqKDQcjyez8d0YV3kE3UwHvh6cbx3P0eO6uuIuFYS8au8GaIxjyxqkrLJFmJlzQvUa16BO16MYBXNDTtbwXyMxT1YCtNXyYtLXmlEKciJaaKyc5QkI0koysXAY9uY4xr37Lc9+XM73iTo/jeXoc1zvS2aROZbQjjd16ohEMWfF/luREuZdnqvbqqnrtr329y0hSHtanU4M858HDduPv7pmntki2TAvh0pJz5SQQEqIRVP9Pn0QhC8Ne9BhTSYjxd/eusN16otEt/kRNvKA7SZa6P2CrLs94u3a34jIHGVn5xEaF+Wy26K/qH/zujty1zZB7C9haurbWnFKI+qgrVw6a9/+2BEJCNCCnrvh620FmvPuj8R+8ao2g3XoiN9lnkaMn0MV0kOXm+SQdm/1x7wprqDYZg3tb+WrmJUy7uGfdg3Etz/jzhuXOF/K1a6u2QKm2D/646HBqex+sHrC1BlVnu1rLLjchGloguXLNQQIhIRqIezlp7GvrOeJwAmCtFgSNdqTxlTqT6x1zvS6F5RF/wkFQ1WAjxKQZuTh1SWwf4dcbVvGRCqYPPsXrFuDFN/U3epAF+sHfVvNpZMu0CHYt/f+2JEsLEYiSvXC4ALoM8Lg7IyuXF5e+C8RClRo/ZURSSAzo1OgP5i6Q2FBLYeA92Aikou2qLfv9ep7uCdF8NfMSn7s/Xrqpf8Db29tyPo1smRbBrKX/35ZASAh/lNvgwDb451VQaYcJGdDlDwA4S3JY+eG7/Dt8PnbCuMz+lFHw8BBRjHPMIpqjNWoAVW2W6jS3h2OzSCfCW7ARSEXbQN6watu15euDHyBzR6HXYKCttyBoDbvchGgMLf3/tgRCQtTFXR3algOV5aA74fWhrmCofRKVL1/Coop8NA1QFSRQ4lH5+RBRtRZIvOfSU7j70lPYsLOINdl5vP71roDq9gzrY2VoH2utswzu5Zm6Zmka8g2r+gd/XVtnpQWBEG1TS/+/HVCOUPfu3dE0rcbX1KlTASgvL2fq1KnEx8fTrl07Ro0aRX5+vsdj7NmzhxEjRhAVFUViYiL3338/lZWVHmO++OIL+vfvT3h4OD179mTJkiU1ruWFF16ge/fuREREMHDgQDZs2OBx3J9rEcIv7urQh3IhOhFMIaBXwutD4OWLCD/qCoIqjzVJ/Qn/kpMBTBpMvbinETTMufIMFnvJJ6nNLandubpfZ1J7xNf6RjK0TxJfzbyEZZPO49nR/Vg26Ty+mnmJ19kjaNjkXn+3zko+jRBtU0v+v60ppfzer3bgwAGczuPT91lZWVx22WV8/vnn/OlPf2LKlCmsXr2aJUuWYLFYmDZtGiaTia+//hoAp9NJv379sFqtPPXUU+Tm5nLLLbcwadIkHnvsMQB27txJnz59uOOOO7jttttYu3Yt9957L6tXr2bIkCEA/Otf/+KWW25h8eLFDBw4kGeeeYYVK1awdetWEhMTAeq8Fn+UlpZisViw2WzExMT4fZ5oI6o2S7XlGNWhaZ8Eh/NBHW+RUalMjDrWKT5QyyadV2PJxKkrnl/7G8+s3V7ruUmWCL6aeUmD/ybVkIXPnLpi0JPrfCZhu2eZqn4fLbH6rBDixDXV/+1APr8DCoSqu/fee1m1ahXbtm2jtLSUjh078s4773DdddcB8Ouvv3L66aeTmZnJeeedx3/+8x+uuOIK9u/fT6dOrsJsixcvZubMmRw4cACz2czMmTNZvXo1WVlZxvOMHj2akpISMjIyABg4cCB/+MMfWLRoEQC6rtO1a1fuuusuZs2ahc1mq/Na/CGBUBBzL4eVHXC1yLB08QyGqpntmMAyfXC9nurZ0f24ul9nj/vqCh7cXryxP8PPbJzfpBrqDStzRyFjXv22znHeAkIhhKiPQD6/67193uFwsHTpUiZMmICmaWzcuJGKigoGDz7+YdCrVy9OOukkMjMzAcjMzKRv375GEAQwZMgQSktL+fnnn40xVR/DPcb9GA6Hg40bN3qMMZlMDB482Bjjz7V4Y7fbKS0t9fgSQabcBrZ9NZqlOov3snF3Cd+fPMXraY+ELeEsap+98cVbgnJd29jd4qLN9XpOf7iX6/xZdqtNS986K4QIbvUOhD788ENKSkoYP348AHl5eZjNZmJjYz3GderUiby8PGNM1SDIfdx9rLYxpaWlHD16lIMHD+J0Or2OqfoYdV2LN48//jgWi8X46tq1a90/CNF2lOyFJVfCkuGAcs0ExXWH4l0UPnsRXd8byoDvZ3qeExWPMoUQpum8F54ecDDkq7hgWwoeWvrWWSFEcKt3IPTaa68xbNgwkpOTG/J6mtXs2bOx2WzG1969e5v7kkRTKbfBv8ZCwc9QvAu1ZAQbdxfzTJeF5OpxJFJEoumQa2cY4FQaeXocHClEi07EyfFgqG8AwZCvxOO2FDxImwkhREtWr0Bo9+7dfPbZZ9x2223GfVarFYfDQUlJicfY/Px8rFarMab6zi337brGxMTEEBkZSUJCAiEhIV7HVH2Muq7Fm/DwcGJiYjy+RJCwH3YFQ3oluhaCVryLhPdG8vP3X9JRK/YY6lQmRtrnMdLxCDl0gkO5HCCGSmXCThgHia3z6WKjwlhcy06JthQ8SJsJIURLVq9A6I033iAxMZERI0YY9w0YMICwsDDWrl1r3Ld161b27NlDamoqAKmpqfz0008UFBQYY9asWUNMTAy9e/c2xlR9DPcY92OYzWYGDBjgMUbXddauXWuM8edaRJBz5wK5WTrjvGUVJRGdMSknFcpEN1MBr4YvJLTa53OBslBAHPuJ5/ryhymN7MJ+PZ6x9lkexRS9aRceyvTBp7Lx4ctq3X3V1oKHlrx1VggR3ALeNabrOikpKYwZM4YnnnjC49iUKVP45JNPWLJkCTExMdx1110AfPPNN8Dx7fPJycksWLCAvLw8br75Zm677bYa2+enTp3KhAkTWLduHXfffXeN7fPjxo3j5Zdf5txzz+WZZ57h3Xff5ddffzVyh+q6Fn/IrrE2qNwGB36D1feB3WbsCMvIyuWFj77kpENbeCb8RcI03eO0AyqGBx0TeTjsbY++YbnEM21AJG9uLKyzR1i0OYQf5lyOOdT/3z8acht7SyDb4oUQTSGQz++AK0t/9tln7NmzhwkTJtQ4tnDhQkwmE6NGjcJutzNkyBBefPFF43hISAirVq1iypQppKamEh0dzbhx43jkkUeMMSkpKaxevZrp06fz7LPP0qVLF/7xj38YQRDADTfcwIEDB5gzZw55eXn069ePjIwMjwTquq5FBKGSvbBsNBz8zVUdWjlRS0bwWo/n+firH3jV/HcSw0vw9rlsV2FkqZON/mDuZqk3ONKIsw7iEEfrfPrJF50cUBAEba9HlbSZEEK0NCdUR6itkxmhNqTcBm9eCflZriAI0DFhQidXjyVRKyXk2CyQUhhJ0QAVSiNMU8YsEGA0S7075GHWPXQ1f3zqc59tKQCiw0PYMndIqw1ghBCiNWmSOkJCtApV6wKV21xBkCkEABM6lQqSTCU1gqAKZeIe+53k6PGEacrIGVpuno8CbnCkMc4xizIt2sjnqe03ijK7kzXZvks3CCGEaB4SCIm2y10dunpdIN1J5bF/+tUTod1B0HX2dD5Sg7jekc5uPZEwTadCmSglijIiySOeQ0RRfKSCb38v5JJenYgOD/F5KRowb2U2Tl0mYIUQoiWRQEi0TSV7YXemR3VoAMavxhGRQCi619MqlYnb7dONnmG5xDPakcZuPZFf1UlMdsyokRQ94Y0NnDnvU8rsTm8PCbi6Lefaytmws6ghvjshhBANJOBkaSFavJK98OJAqLTDn9+CTx88HgxdMJ2w8oNeT6tQEKbpzDX/k18c3cnFldSbSzw3ONIoI9LrzjC7U4HTv5me1lAJWgghgonMCIm253CBKwjSK1Hv3sIvZ86mLKqrKxhadU+NujxO5fpvEKa5EqPduUBWCo0x7qWwE9UaKkELIUQwkUBItH7ViyN2GYBz/H9waiFoeiWnfDGFt2xn1jitUkGuHkeIplNpBEOKCqVRShRHiGTShSk+qzsHojVVghZCiGAigZBo3aokRDuL95K5o5BHVv7MWa8VM/novSgFoZrOlLDVHqdVKJhsv4+RjkfYrScSeiwYsqsQflNdmeyYwWOjz+ehEb156ab+xEaGnfCltqZK0EIIESwkEBKtm/2wkRCd++ylzHh1Na9/vYv29nzmmP/pUQ8IoFRFkqdbCNNgrvmfAEYy9C/qJK63z2G0Yw5XXfQHrujXGXAVNXzhxv71vsQO0WHSRkIIIVooCYRE62bpzBepb7BbT6QL+Sw3z6e/9ptR/bl6udBIynnIcRu79cQadYFudDxMTvQZPHnjIGYP7+1x3nk94kmyBJ7fEx9t5tvZgyUIEkKIFkoCIdGqOXXF7LXFxqxON1MB74enG0GQprmKJD7huIEKZSJMUywOX8gjjpvZrSdSSAxlRDLiggG8MukSvntoMMPPrBm0uIsmBrKwpQGPXtsn4LYaQgghmo68Q4tWbcPOInJt5eQSz/SKOz2OaZqrLtBE+30s1q/mOnv6sWBIZ3H4QuY6bmacYxaHiOKTrLw6e3i5O6jHRtWdL5QkXdWFEKJVkDpColVz1+VJopCFYZ5NdZWC2+3TWccAAH6kJ9fZ03kvPB07YWylm7El3l3ssK6GoO4mqIvWbeONr3dRcrTCOBYfbebqfslc1tvaqhujCiFEMJFASLRqie0jSKLQyAnarScyveJOng17jq6mIuaY/0l2leKIP9KTkfZ0DhJLLgkej+VvscMQk8Y9g09l2iWntJmu8EIIEaxkaUy0aufGH2VFxF+NIGi0I41N6lT+7JjnkRBdtTjiT/SsEQRB4MUOQ0waqT3iubpfZ1J7xEsQJIQQrZAEQqJVC4loT7sOVnbriYxxpHm0xXAnULsTon2RYodCCBG8ZGlMtG4RFmInr+SHLb+j1haB7fjyVl09wqqSYodCCBGcJBASrYJTV77zcSIsXHzu2Xx1zvEx2/IPs+jz7eRRe/JzdHgIT406U3Z3CSFEkJJASLR4GVm5zFuZTW6V2Z4kSwRzr+ztEcC4c3YAMncUsujz7XU+dpndyfzVv2AyaRIMCSFEEJIcIdGifbIllzuWbvIIggDybOVMWbqJjKxcr+edm9KBJEuEXwUQ63osIYQQbZcEQqL5VO8aX5VtH//duJVpyzZ5PezunDFvZTZOXdU47q4EXfNI4I8lhBCi7ZJASDSPKl3jseV4HrPlcOSVISR8dCPR6ojPh1AcL4Toiz9VoP19LCGEEG2PBEKieVTpGs+SEceDIVsOaskIosr2Ek8p0Ryt86G8FULMyMplytJNlByp8HJGYI8lhBCi7ZJASDQPS2cYvxriuh8PhvashyUj0Ip3GcUR69r1BccLITp1ReaOQj74YR8PfvCTX8tivh5LCCFEcJBdY6L5WLq4gqElI1zB0OuXA1AW1ZXRRQ8YxRFr4y6E6G1nWSA0wCpFFYUQIujIjJBocu6Zm4827yPzYCTOa172OP77hX/3KwgCVyHENdl5TPGys8xf7p1lUlRRCCGCj8wIiSaVkZVL+sfZ5JUe7xq/IuKvdKkyps+G+zkr5kG2lLbzubxl0mDRmP5c1tvKoCfX1WsZzM3qpSaREEKI4CCBkGgyGVmumkBu7q7xXXA1TM295BnO+/FBtOJdLIuez2Bcy2PegpxFY85m+JlJZO4orNdMUIfoMNKuOANrjHSNF0KIYCZLY6JJOHXFrPd/Mm5bjwVBVbvGT/kyDOctqyCuO1Fle/ksfgF9Yw57PE6SJYLFN/Vn+JnJQOC7vLRjX49d25drz5au8UIIEexkRkg0iW9/L/TYyl5GJIXEgA6j3V3jj1TwbWEkFxxLoI6K7sgHNw5hQ67Te48xAt/lJctgQgghqpJASDSJzB2FHrcPEcU4xyyiOeqxRT5zRyEX9DwNxn8C4e0IibCQ2sP347pbaeTZyr0uoWlAh2gzD484HaslUpbBhBBCeJBASDQod5f4PNtRisocdGgXjjUmAl3pNcYeIopDRFW791g4Y+ns1/O5W2lMWboJ7fjZwPHdYI9e20dmgIQQQnglgZBoMNV3hFXVIdrs12OknpwQ8PMO7ZPESzf1r1FHSJbBhBBC1EUCIdEgqu8Iq66ozFHnY8RGhXFeD//qB1U3tE8Sl/W2smFnkc98IiGEEKI6CYREvTkqdf6ZuYtdhWWs2JhT9wl1eGJk3xMKXEJMGqn1DKSEEEIEJwmERL08/kk2r/5vJ3o9KhnGRYVRXGUHmTUmnPSrzpAlLCGEEE1OAiERsMc/yeblL3fW+/w5V7oKGcoSlhBCiOYmgZDwrtwG9sM1dm85KnVW/u972hPpZceXf6wxEbKEJYQQokWQytKipnIbLB0FS4aDzTP35/3Pv2VZ2HzeND9Be44E/NBJ0uFdCCFECyKBkKjJfhjKDkDxLlgy4ngwZMvh8u8m0s1UQDylRHM0oIfVkA7vQgghWhYJhERNls4wfjXEdT8eDO1ZD0tG0MGx3+gNVrUidF3iosJ46ab+khAthBCiRZEcIeGVs31nNl+8lF4ZY4gu3gWvXw7A4aiujCl2dYX3hyUylAkXpDDtklNkJkgIIUSLI4GQqCEjK9eo0txfm8j74enGsVuKJ7Jf1R4EXXFmEpf17iQ7woQQQrR4EggJDxlZuUxZugkFJFHIwrAXPY4vDHvxeLf4akwaTLowhdnDezfR1QohhBAnRgIhAbiapX77eyGz/v2TEQQtN8+nm6mA3Xoi0yvuZGHYi3QzFbDcPN8IhmIiQrm6XzLd46O5ObU75lBJOxNCCNF6SCAkPJbCAKzVgiB30DPakWbcv9w8nxscaeSVxzO8b7LUBRJCCNEqya/vQc69FFa1a3sZkRQS4xEEAUYwtFtPpJAYyogEoOBQzW7zQgghRGsgM0JByqkrvt1xfCmsqkNEMc4xi2iO1tgin0s8NzjSKKtSWTqxfUQTXbUQQgjRsCQQCkLVl8K8OUSUzxYa7uBIA6xSKVoIIUQrJoFQkKm6K+xEuDfES6VoIYQQrZkEQkHEqSvmrcw+4SAIXDNBc6/sLZWihRBCtGoSCAWRDTuLal0O89e0i3sy/bJTZSZICCFEqye7xoJIQ+3uuqBnggRBQggh2gSZEQoiJ7q7S5KjhRBCtDUyIxREzk3pQJIlgtrmcqLMIV7vl+RoIYQQbZEEQkEkxKQx90pXH7DqoYx27Ovvfz6LxTf1J8niOXtktUTw0k39JTlaCCFEm6IppRpiE1GbVFpaisViwWazERMT09yX02C81RFKqrYLzKkrNuwsouBQuXSRF0II0aoE8vktgVAt2mogBBLoCCGEaLsC+fyWZOkgFWLSpFGqEEKIoCeBkJDZISGEEEFLAqEg50++kBBCCNFWya6xIObuO1a92nSerZwpSzeRkZXbTFcmhBBCNA0JhIJUbX3H3PfNW5mNU5dceiGEEG2XBEJBqq6+YwrItZWzYWdR012UEEII0cQkEApS/vYda6j+ZEIIIURLJIFQkPK379iJ9icTQgghWjIJhIJUXX3HNFy7x6TBqhBCiLZMAqEgVVffMZAGq0IIIdq+gAOhffv2cdNNNxEfH09kZCR9+/bl+++/N46PHz8eTdM8voYOHerxGEVFRYwdO5aYmBhiY2OZOHEihw8f9hizZcsWLrzwQiIiIujatSsLFiyocS0rVqygV69eRERE0LdvXz755BOP40op5syZQ1JSEpGRkQwePJht27YF+i03nHIb2PZ5P2bb5zrehIb2SeKlm/pjlQarQgghglRABRWLi4u54IILuPjii/nPf/5Dx44d2bZtG3FxcR7jhg4dyhtvvGHcDg8P9zg+duxYcnNzWbNmDRUVFdx6661MnjyZd955B3D1CLn88ssZPHgwixcv5qeffmLChAnExsYyefJkAL755hvGjBnD448/zhVXXME777zDNddcw6ZNm+jTpw8ACxYs4LnnnuPNN98kJSWFtLQ0hgwZQnZ2NhERTZz7Um6DpaOg7ACMXw2WLseP2XJgyQiI7gg3/RsiLE12WUP7JHFZb6tUlhZCCBGcVABmzpypBg0aVOuYcePGqauvvtrn8ezsbAWo7777zrjvP//5j9I0Te3bt08ppdSLL76o4uLilN1u93ju0047zbj95z//WY0YMcLjsQcOHKhuv/12pZRSuq4rq9WqnnrqKeN4SUmJCg8PV8uWLav7m1VK2Ww2BSibzebX+FqV5Cj1zJlKzY1x/Vmy99j9e6vdn+P19Eqnrr7ZflB9+EOO+mb7QVXp1E/8moQQQog2KJDP74CWxj7++GPOOeccrr/+ehITEzn77LN59dVXa4z74osvSExM5LTTTmPKlCkUFhYaxzIzM4mNjeWcc84x7hs8eDAmk4n169cbYy666CLMZrMxZsiQIWzdupXi4mJjzODBgz2ed8iQIWRmZgKwc+dO8vLyPMZYLBYGDhxojKnObrdTWlrq8dVgLJ1dM0Fx3aF4l2sGaM9615/Fu1z3j1/tGldNRlYug55cx5hXv+We5ZsZ8+q3DHpynVR+FkIIIU5QQIHQ77//zksvvcQpp5zCp59+ypQpU7j77rt58803jTFDhw7lrbfeYu3atTz55JP83//9H8OGDcPpdAKQl5dHYmKix+OGhobSoUMH8vLyjDGdOnXyGOO+XdeYqsernudtTHWPP/44FovF+Oratav/Pxx/WLp4BkOvX14tCDq+XObUFZk7Cpm/8mfukDYYQgghRKMIKEdI13XOOeccHnvsMQDOPvtssrKyWLx4MePGjQNg9OjRxvi+ffty5pln0qNHD7744gsuvfTSBrz0hjd79mxmzJhh3C4tLW2cYOjaV1xBkNu1r3gEQd4aoVancO3umrcym8t6WyWnRwghhKiHgGaEkpKS6N27t8d9p59+Onv27PF5zsknn0xCQgLbt28HwGq1UlBQ4DGmsrKSoqIirFarMSY/P99jjPt2XWOqHq96nrcx1YWHhxMTE+Px1eBsOfDBZM/7Ppjsuh/fjVC9kTYYQgghxIkJKBC64IIL2Lp1q8d9v/32G926dfN5Tk5ODoWFhSQlubZip6amUlJSwsaNG40x69atQ9d1Bg4caIz58ssvqaioMMasWbOG0047zdihlpqaytq1az2ea82aNaSmpgKQkpKC1Wr1GFNaWsr69euNMU3OvTvMvRw24b8eOUPO4r0+G6HWRtpgCCGEEPUTUCA0ffp0vv32Wx577DG2b9/OO++8wyuvvMLUqVMBOHz4MPfffz/ffvstu3btYu3atVx99dX07NmTIUOGAK4ZpKFDhzJp0iQ2bNjA119/zbRp0xg9ejTJyckA3HjjjZjNZiZOnMjPP//Mv/71L5599lmPZat77rmHjIwMnn76aX799VfS09P5/vvvmTZtGgCapnHvvffy17/+lY8//piffvqJW265heTkZK655pqG+NkFxravZmL0SQM9coYqXhuO8lVnqBbSBkMIIYSon4ACoT/84Q988MEHLFu2jD59+jB//nyeeeYZxo4dC0BISAhbtmzhqquu4tRTT2XixIkMGDCA//3vfx61hN5++2169erFpZdeyvDhwxk0aBCvvPKKcdxisfDf//6XnTt3MmDAAO677z7mzJlj1BACOP/8841A7KyzzuK9997jww8/NGoIATzwwAPcddddTJ48mT/84Q8cPnyYjIyMpq8hBBDezlUnqHpidJUE6iNhcZQR6fdDShsMIYQQ4sRoSqlAV2KCRmlpKRaLBZvN1jD5QuU2sB/2ukUe2z427Hfw5zez/Xood2q0VIAWQgghPAXy+R3QrjFxgiIsvqtGWzozoL0iyfI7ebbyOvOErJYI5l7ZW4IgIYQQ4gRIINSCuBuhTlm6CQ08giH37QkXdOey3lZpgyGEEEI0AOk+38LU1gh18U39mXPlGaT2iJcgSAghhGgAMiPUAkkjVCGEEKJpSCDUQoWYNFJ7xDf3ZQghhBBtmiyNCSGEECJoyYxQC+LUlSyHCSGEEE1IAqEWwluj1STZIi+EEEI0KlkaawF8NVrNs5UzZekmMrJym+nKhBBCiLZNAqFm5tSVz0ar7vvmrczGqUsBcCGEEKKhSSDUzDbsLKoxE1SVAnJt5WzYWdR0FyWEEEIECQmEmlnBId9BUH3GCSGEEMJ/Egg1s8T2EXUPCmCcEEIIIfwngVAzOzelA0mWCHxtktdw7R47N6VDU16WEEIIERQkEGpm7karQI1gyH177pW9pZ6QEEII0QgkEGoBamu0+tJN/aWOkBBCCNFIpKBiCyGNVoUQQoimJ4FQCyKNVoUQQoimJUtjQgghhAhaEggJIYQQImhJICSEEEKIoCWBkBBCCCGClgRCQgghhAhaEggJIYQQImhJICSEEEKIoCWBkBBCCCGClgRCQgghhAhaUlm6FkopAEpLS5v5SoQQQgjhL/fntvtzvDYSCNXi0KFDAHTt2rWZr0QIIYQQgTp06BAWi6XWMZryJ1wKUrqus3//ftq3b4+mNXzz09LSUrp27crevXuJiYlp8McXjUNet9ZHXrPWR16z1qmlvG5KKQ4dOkRycjImU+1ZQDIjVAuTyUSXLl0a/XliYmLkP3orJK9b6yOvWesjr1nr1BJet7pmgtwkWVoIIYQQQUsCISGEEEIELQmEmlF4eDhz584lPDy8uS9FBEBet9ZHXrPWR16z1qk1vm6SLC2EEEKIoCUzQkIIIYQIWhIICSGEECJoSSAkhBBCiKAlgZAQQgghgpYEQnXYt28fN910E/Hx8URGRtK3b1++//574/j48ePRNM3ja+jQoR6PUVRUxNixY4mJiSE2NpaJEydy+PBhjzFbtmzhwgsvJCIigq5du7JgwYIa17JixQp69epFREQEffv25ZNPPvE4rpRizpw5JCUlERkZyeDBg9m2bVsD/jRah+7du9d4TTRNY+rUqQCUl5czdepU4uPjadeuHaNGjSI/P9/jMfbs2cOIESOIiooiMTGR+++/n8rKSo8xX3zxBf379yc8PJyePXuyZMmSGtfywgsv0L17dyIiIhg4cCAbNmzwOO7PtQSDul6zP/3pTzWO3XHHHR6PIa9Z03I6naSlpZGSkkJkZCQ9evRg/vz5Hr2d/HlPkvfHpuPPaxaUn2lK+FRUVKS6deumxo8fr9avX69+//139emnn6rt27cbY8aNG6eGDh2qcnNzja+ioiKPxxk6dKg666yz1Lfffqv+97//qZ49e6oxY8YYx202m+rUqZMaO3asysrKUsuWLVORkZHq5ZdfNsZ8/fXXKiQkRC1YsEBlZ2erhx9+WIWFhamffvrJGPPEE08oi8WiPvzwQ/Xjjz+qq666SqWkpKijR4824k+p5SkoKPB4PdasWaMA9fnnnyullLrjjjtU165d1dq1a9X333+vzjvvPHX++ecb51dWVqo+ffqowYMHqx9++EF98sknKiEhQc2ePdsY8/vvv6uoqCg1Y8YMlZ2drZ5//nkVEhKiMjIyjDHLly9XZrNZvf766+rnn39WkyZNUrGxsSo/P98YU9e1BIu6XrM//vGPatKkSR5jbDabcb68Zk3v0UcfVfHx8WrVqlVq586dasWKFapdu3bq2WefNcb4854k749Nx5/XLBg/0yQQqsXMmTPVoEGDah0zbtw4dfXVV/s8np2drQD13XffGff95z//UZqmqX379imllHrxxRdVXFycstvtHs992mmnGbf//Oc/qxEjRng89sCBA9Xtt9+ulFJK13VltVrVU089ZRwvKSlR4eHhatmyZXV/s23YPffco3r06KF0XVclJSUqLCxMrVixwjj+yy+/KEBlZmYqpZT65JNPlMlkUnl5ecaYl156ScXExBiv0QMPPKDOOOMMj+e54YYb1JAhQ4zb5557rpo6dapx2+l0quTkZPX4448rpZRf1xKsqr5mSrkCoXvuucfneHnNmt6IESPUhAkTPO4bOXKkGjt2rFLKv/ckeX9sWnW9ZkoF52eaLI3V4uOPP+acc87h+uuvJzExkbPPPptXX321xrgvvviCxMRETjvtNKZMmUJhYaFxLDMzk9jYWM455xzjvsGDB2MymVi/fr0x5qKLLsJsNhtjhgwZwtatWykuLjbGDB482ON5hwwZQmZmJgA7d+4kLy/PY4zFYmHgwIHGmGDkcDhYunQpEyZMQNM0Nm7cSEVFhcfPqVevXpx00knGzykzM5O+ffvSqVMnY8yQIUMoLS3l559/NsbU9no4HA42btzoMcZkMjF48GBjjD/XEoyqv2Zub7/9NgkJCfTp04fZs2dz5MgR45i8Zk3v/PPPZ+3atfz2228A/Pjjj3z11VcMGzYM8O89Sd4fm1Zdr5lbsH2mSdPVWvz++++89NJLzJgxgwcffJDvvvuOu+++G7PZzLhx4wAYOnQoI0eOJCUlhR07dvDggw8ybNgwMjMzCQkJIS8vj8TERI/HDQ0NpUOHDuTl5QGQl5dHSkqKxxj3G3peXh5xcXHk5eV5vMm7x1R9jKrneRsTjD788ENKSkoYP3484Po5mc1mYmNjPcZV/1l6+zm6j9U2prS0lKNHj1JcXIzT6fQ65tdff/X7WoJR9dcM4MYbb6Rbt24kJyezZcsWZs6cydatW3n//fcBec2aw6xZsygtLaVXr16EhITgdDp59NFHGTt2LODfe5K8Pzatul4zCM7PNAmEaqHrOueccw6PPfYYAGeffTZZWVksXrzYCIRGjx5tjO/bty9nnnkmPXr04IsvvuDSSy9tlusWx7322msMGzaM5OTk5r4U4Sdvr9nkyZONv/ft25ekpCQuvfRSduzYQY8ePZrjMoPeu+++y9tvv80777zDGWecwebNm7n33ntJTk423h9Fy+LPaxaMn2myNFaLpKQkevfu7XHf6aefzp49e3yec/LJJ5OQkMD27dsBsFqtFBQUeIyprKykqKgIq9VqjKm+68R9u64xVY9XPc/bmGCze/duPvvsM2677TbjPqvVisPhoKSkxGNs9Z9lfV+PmJgYIiMjSUhIICQkpM7XrK5rCTbeXjNvBg4cCODx/0xes6Z1//33M2vWLEaPHk3fvn25+eabmT59Oo8//jjg33uSvD82rbpeM2+C4TNNAqFaXHDBBWzdutXjvt9++41u3br5PCcnJ4fCwkKSkpIASE1NpaSkhI0bNxpj1q1bh67rxpt5amoqX375JRUVFcaYNWvWcNpppxEXF2eMWbt2rcdzrVmzhtTUVABSUlKwWq0eY0pLS1m/fr0xJti88cYbJCYmMmLECOO+AQMGEBYW5vFz2rp1K3v27DF+Tqmpqfz0008e/9nXrFlDTEyMERjX9XqYzWYGDBjgMUbXddauXWuM8edago2318ybzZs3A3j8P5PXrGkdOXIEk8nzIyQkJARd1wH/3pPk/bFp1fWaeRMUn2kNmnrdxmzYsEGFhoaqRx99VG3btk29/fbbKioqSi1dulQppdShQ4fUX/7yF5WZmal27typPvvsM9W/f391yimnqPLycuNxhg4dqs4++2y1fv169dVXX6lTTjnFY6thSUmJ6tSpk7r55ptVVlaWWr58uYqKiqqx1TA0NFT97W9/U7/88ouaO3eu162GsbGx6qOPPlJbtmxRV199ddBtD3VzOp3qpJNOUjNnzqxx7I477lAnnXSSWrdunfr+++9VamqqSk1NNY67t2JffvnlavPmzSojI0N17NjR61bs+++/X/3yyy/qhRde8LoVOzw8XC1ZskRlZ2eryZMnq9jYWI+dTXVdSzDx9Zpt375dPfLII+r7779XO3fuVB999JE6+eST1UUXXWSMkdes6Y0bN0517tzZ2Ir9/vvvq4SEBPXAAw8YY/x5T5L3x6ZT12sWrJ9pEgjVYeXKlapPnz4qPDxc9erVS73yyivGsSNHjqjLL79cdezYUYWFhalu3bqpSZMmebxpKqVUYWGhGjNmjGrXrp2KiYlRt956qzp06JDHmB9//FENGjRIhYeHq86dO6snnniixrW8++676tRTT1Vms1mdccYZavXq1R7HdV1XaWlpqlOnTio8PFxdeumlauvWrQ3402g9Pv30UwV4/f6PHj2q7rzzThUXF6eioqLUtddeq3Jzcz3G7Nq1Sw0bNkxFRkaqhIQEdd9996mKigqPMZ9//rnq16+fMpvN6uSTT1ZvvPFGjed6/vnn1UknnaTMZrM699xz1bfffhvwtQQLX6/Znj171EUXXaQ6dOigwsPDVc+ePdX999/vUUdIKXnNmlppaam655571EknnaQiIiLUySefrB566CGPLdP+vCfJ+2PTqes1C9bPNE2pKiUlhRBCCCGCiOQICSGEECJoSSAkhBBCiKAlgZAQQgghgpYEQkIIIYQIWhIICSGEECJoSSAkhBBCiKAlgZAQQgghgpYEQkIIIYQIWhIICSGEECJoSSAkhBBCiKAlgZAQQgghgpYEQkIIIYQIWv8P2w8o40yoELMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 0\n",
      "reclassifying HD 1510\n",
      "reclassifying HD 3097\n",
      "reclassifying HD 4690\n",
      "reclassifying HD 6489\n",
      "There are 212 HDs still with both discontinuity problems\n",
      "Additionally, 985 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "8dc2ffc7-f1bd-463d-b3d3-0eb332fa0d65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets classify the noncontig by VRA type for badDiscoList and stillHasEnclave\n",
      "VRA,env,other\n",
      "0\n",
      "0\n",
      "212\n",
      "VRA,env,other\n",
      "0\n",
      "0\n",
      "985\n"
     ]
    }
   ],
   "source": [
    "print(\"lets classify the noncontig by VRA type for badDiscoList and stillHasEnclave\")\n",
    "for LIST in [badDiscoList, stillHasEnclave]:\n",
    "    print(\"VRA,env,other\")\n",
    "    for LHD in [coreVTDs, envelopeVs,HDsToDraw]:\n",
    "        print(len(set(LHD).intersection(set(LIST))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca753fc4-2d1d-4f31-8689-720e8f724bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#note -- above triage list may not have included the original list of enclave-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "5b41c8e4-912c-4ed8-8bdc-1820f9cc1d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 773550 district pop vs 736714 target\n",
      "working on enclave-y HD 4 . We have evaluated 0 of 985 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 776 . We have evaluated 100 of 985 enclavy HDs.Time is now 27\n",
      "working on enclave-y HD 1478 . We have evaluated 200 of 985 enclavy HDs.Time is now 48\n",
      "working on enclave-y HD 2392 . We have evaluated 300 of 985 enclavy HDs.Time is now 71\n",
      "working on enclave-y HD 3082 . We have evaluated 400 of 985 enclavy HDs.Time is now 96\n",
      "working on enclave-y HD 3807 . We have evaluated 500 of 985 enclavy HDs.Time is now 112\n",
      "working on enclave-y HD 4270 . We have evaluated 600 of 985 enclavy HDs.Time is now 137\n",
      "working on enclave-y HD 5030 . We have evaluated 700 of 985 enclavy HDs.Time is now 160\n",
      "working on enclave-y HD 5815 . We have evaluated 800 of 985 enclavy HDs.Time is now 192\n",
      "working on enclave-y HD 6582 . We have evaluated 900 of 985 enclavy HDs.Time is now 215\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'internals' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[348], line 26\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     25\u001b[0m             cantFill\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[1;32m---> 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOut of\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mlen\u001b[39m(\u001b[43minternals\u001b[49m\u001b[38;5;241m+\u001b[39medgers),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHDs with enclaves\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mlen\u001b[39m(tryToFill),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhad enclaves.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOf these,\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mlen\u001b[39m(canFill),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt be over\u001b[39m\u001b[38;5;124m\"\u001b[39m,maxPostFixPop,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif all enclaves filled, while\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mlen\u001b[39m(cantFill),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwere too populous\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     28\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter([HDvPop[t] \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m canFill],[totalEnclavePop[t] \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m canFill])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'internals' is not defined"
     ]
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(stillHasEnclave):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(stillHasEnclave),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(stillHasEnclave),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e1a746c-33f0-4c89-a2b1-a01508f679d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#note -- above triage list may not have included the original list of enclave-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "7675242c-1188-44ac-99cb-c9aba0842ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 985 HDs with enclaves 985 had enclaves.\n",
      "Of these, 900 wouldn't be over 773550 if all enclaves filled, while 85 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Out of\",len(stillHasEnclave),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "3c16d430-1887-46f4-ab03-fa348cae7e2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do any of our complement-discontig HDs have a VRA core enclave?\n",
      "checking for coreVRA enclave for HD 12 out of 297\n",
      "checking for coreVRA enclave for HD 1435 out of 297\n",
      "checking for coreVRA enclave for HD 2962 out of 297\n",
      "checking for coreVRA enclave for HD 5414 out of 297\n",
      "checking for coreVRA enclave for HD 6356 out of 297\n",
      "checking for coreVRA enclave for HD 2613 out of 297\n",
      "there were a total of 13 HDs that have a coreVRA enclave\n"
     ]
    }
   ],
   "source": [
    "hasCoreEnclave = list()\n",
    "print(\"do any of our complement-discontig HDs have a VRA core enclave?\")\n",
    "for i,t in enumerate(badDiscoList + cantFill):\n",
    "    if i%50 == 0:\n",
    "        print(\"checking for coreVRA enclave for HD\",t,\"out of\",len(badDiscoList + cantFill) )\n",
    "    enclaveLists = getEnclaveLists(HDunitList[t],unitNbrs)\n",
    "    for eL in enclaveLists:\n",
    "        if len(coreUset.intersection(set(eL)) )  > 0:\n",
    "            hasCoreEnclave.append(t)\n",
    "            break\n",
    "print(\"there were a total of\",len(hasCoreEnclave),\"HDs that have a coreVRA enclave\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "b09991e9-8b48-462d-8f33-e255b59a5d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of these, 13 are enclaveOnly (are contig)\n"
     ]
    }
   ],
   "source": [
    "print(\"of these,\",len(set(hasCoreEnclave).intersection(set(cantFill))),\"are enclaveOnly (are contig)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "5553b5dc-9068-4f57-b8cc-52c3b0cbb04f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.01339 0.12207\n",
      "CCB cluster 0 = unit 2970 with pop 13737.0 now has use of 1.0202849881857254\n",
      "CCB cluster 1 = unit 2971 with pop 169151.0 now has use of 1.0124949989124006\n",
      "CCB cluster 2 = unit 2972 with pop 171003.0 now has use of 1.040815322348304\n",
      "CCB cluster 3 = unit 2973 with pop 92501.0 now has use of 1.021328811456537\n",
      "CCB cluster 4 = unit 2974 with pop 51938.0 now has use of 1.0200433749969038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "4d73e940-678e-4d9b-afea-56d6ea1bc5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "#HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "3778bd30-15c4-466d-accd-0da3ee80c72f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 85 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "985174cd-525e-4abd-8f85-7259f9e288e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the barredJettisonSet (cannot be shed in below MUSTFREE code) is {2970, 2971, 2972, 2973, 2974}\n",
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1600\n",
      "working on avg unit-to-HDcp dist for HD 2400\n",
      "working on avg unit-to-HDcp dist for HD 3200\n",
      "working on avg unit-to-HDcp dist for HD 4002\n",
      "working on avg unit-to-HDcp dist for HD 4802\n",
      "working on avg unit-to-HDcp dist for HD 5602\n",
      "working on avg unit-to-HDcp dist for HD 6402\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set(list())\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u]% 1 > 0.23 and allUnits[u]%1 < 0.27:\n",
    "        barredJettisonSet.add(u)\n",
    "print(\"the barredJettisonSet (cannot be shed in below MUSTFREE code) is\",barredJettisonSet)\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "2cae9189-626c-4f57-976b-8a82bdbbb39a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How tight are our current unit usages in the 3 counties in St L area?\n",
      "unit usages for units in county 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"How tight are our current unit usages around Milwaukee?\")\n",
    "coreUnitUse, envUnitUse, otherUnitUse = [0.]*nUnits, [0.]*nUnits, [0.]*nUnits\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        if t in coreVTDs:\n",
    "            coreUnitUse[u] += nDistricts * HDweight[t]\n",
    "        elif t in envelopeVs:\n",
    "            envUnitUse[u] += nDistricts * HDweight[t]\n",
    "        else:\n",
    "            otherUnitUse[u] += nDistricts * HDweight[t]\n",
    "for c in VRAcounties:\n",
    "    print(\"unit usages for units in county\",c)\n",
    "    plt.hist([coreUnitUse[u] for u in countyUnitList[c]],label=\"usage by coreVTDs\",histtype=\"step\",bins=50)\n",
    "    plt.hist([envUnitUse[u] for u in countyUnitList[c]],label=\"usage by envVTDs\",histtype=\"step\",bins=50)\n",
    "    plt.hist([otherUnitUse[u] for u in countyUnitList[c]],label=\"usage by other HDs\",histtype=\"step\",bins=50)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "8a73f6b1-4ad1-4bf1-9c6a-1a1d59657ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 49\n",
      "working on HD 1400 time is now 101\n",
      "working on HD 2100 time is now 143\n",
      "working on HD 2800 time is now 184\n",
      "working on HD 3500 time is now 233\n",
      "working on HD 4202 time is now 291\n",
      "working on HD 4902 time is now 340\n",
      "working on HD 5602 time is now 396\n",
      "working on HD 6302 time is now 444\n",
      "working on HD 7002 time is now 496\n",
      "out of 7057 total HDs, there were 6845.0 contiguous and 4691.0 complement-contiguous HDs\n",
      "105 HDs had both discontiguity problems, while enclave-only = 2261 and discontig only= 107\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"updated classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "44ef59ac-a53b-4e93-91fc-aea1ee636246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for 389 HD VTDs within coreVTDs ... \n",
      "0 at least discontig\n",
      "2 encl only\n",
      "0 bothProb\n",
      "for 36 HD VTDs within envelopeVs ... \n",
      "0 at least discontig\n",
      "3 encl only\n",
      "0 bothProb\n",
      "for 6632 HD VTDs within other HD centers ... \n",
      "212 at least discontig\n",
      "2256 encl only\n",
      "105 bothProb\n"
     ]
    }
   ],
   "source": [
    "setName = [\"coreVTDs\",\"envelopeVs\",\"other HD centers\"]\n",
    "for jj,SET in enumerate([set(coreVTDs), set(envelopeVs), set(HDsToDraw)]):\n",
    "    print(\"for\",len(SET), \"HD VTDs within\",setName[jj],\"... \")\n",
    "    for i, LIST in enumerate([sPgenerator,eLgenerator,doubleTroubleList]):\n",
    "        NAME = [\"at least discontig\", \"encl only\",\"bothProb\"]\n",
    "        print(len(set(LIST).intersection(SET)), NAME[i] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "15960844-19c6-4d0a-8c92-251471fd8ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "re-running the enclave-filling code, not sure why needed ...\n",
      "Now, let's fill in all enclaves that won't put us over 773550 district pop vs 736714 target\n",
      "working on enclave-y HD 2 . We have evaluated 0 of 2261 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 422 . We have evaluated 100 of 2261 enclavy HDs.Time is now 19\n",
      "working on enclave-y HD 741 . We have evaluated 200 of 2261 enclavy HDs.Time is now 47\n",
      "working on enclave-y HD 1059 . We have evaluated 300 of 2261 enclavy HDs.Time is now 64\n",
      "working on enclave-y HD 1338 . We have evaluated 400 of 2261 enclavy HDs.Time is now 82\n",
      "working on enclave-y HD 1688 . We have evaluated 500 of 2261 enclavy HDs.Time is now 111\n",
      "working on enclave-y HD 2027 . We have evaluated 600 of 2261 enclavy HDs.Time is now 128\n",
      "working on enclave-y HD 2385 . We have evaluated 700 of 2261 enclavy HDs.Time is now 146\n",
      "working on enclave-y HD 2606 . We have evaluated 800 of 2261 enclavy HDs.Time is now 161\n",
      "working on enclave-y HD 2927 . We have evaluated 900 of 2261 enclavy HDs.Time is now 179\n",
      "working on enclave-y HD 3196 . We have evaluated 1000 of 2261 enclavy HDs.Time is now 199\n",
      "working on enclave-y HD 3433 . We have evaluated 1100 of 2261 enclavy HDs.Time is now 212\n",
      "working on enclave-y HD 3665 . We have evaluated 1200 of 2261 enclavy HDs.Time is now 231\n",
      "working on enclave-y HD 3903 . We have evaluated 1300 of 2261 enclavy HDs.Time is now 251\n",
      "working on enclave-y HD 4150 . We have evaluated 1400 of 2261 enclavy HDs.Time is now 269\n",
      "working on enclave-y HD 4436 . We have evaluated 1500 of 2261 enclavy HDs.Time is now 290\n",
      "working on enclave-y HD 4769 . We have evaluated 1600 of 2261 enclavy HDs.Time is now 304\n",
      "working on enclave-y HD 4985 . We have evaluated 1700 of 2261 enclavy HDs.Time is now 332\n",
      "working on enclave-y HD 5265 . We have evaluated 1800 of 2261 enclavy HDs.Time is now 360\n",
      "working on enclave-y HD 5816 . We have evaluated 1900 of 2261 enclavy HDs.Time is now 391\n",
      "working on enclave-y HD 6156 . We have evaluated 2000 of 2261 enclavy HDs.Time is now 408\n",
      "working on enclave-y HD 6624 . We have evaluated 2100 of 2261 enclavy HDs.Time is now 433\n",
      "working on enclave-y HD 6834 . We have evaluated 2200 of 2261 enclavy HDs.Time is now 456\n"
     ]
    }
   ],
   "source": [
    "print(\"re-running the enclave-filling code, I think I missed a list to triage ...\")\n",
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "stillHasEnclave = eLgenerator.copy()\n",
    "\n",
    "for iii, t in enumerate(stillHasEnclave):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(stillHasEnclave),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(stillHasEnclave),len(canFill),\"wouldn't be over\",maxPostFixPop,\n",
    "      \"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "3d73cb24-37dc-4cbb-bb18-e95067c7a00c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 2261 1940 wouldn't be over 773550 if all enclaves filled, while 321 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Out of\",len(stillHasEnclave),len(canFill),\"wouldn't be over\",maxPostFixPop,\n",
    "      \"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b760b565-a3d8-4968-8249-dad5f0a66c67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "27dd9f73-ef6e-425a-8c59-a5ce34c7324c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 16\n",
      "working on HD 1400 time is now 32\n",
      "working on HD 2100 time is now 42\n",
      "working on HD 2800 time is now 56\n",
      "working on HD 3500 time is now 65\n",
      "working on HD 4202 time is now 78\n",
      "working on HD 4902 time is now 90\n",
      "working on HD 5602 time is now 103\n",
      "working on HD 6302 time is now 119\n",
      "working on HD 7002 time is now 132\n",
      "out of 7057 total HDs, there were 6845.0 contiguous and 6631.0 complement-contiguous HDs\n",
      "105 HDs had both discontiguity problems, while enclave-only = 321 and discontig only= 107\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"updated classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "a722c870-a864-4405-a415-6fe03cef9006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the contig pops for discontig Us relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here are the HD centers with contig pop < 0.7 * aDP ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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JjIw0fO/v7w+1Wg2VSoWKigqcOHECaWlpmDdvnnQFEhEZGQOKCWILCt3LlClTDN+HhIRAp9Ph2LFjaG1txbJlyySsjIjIeNhJ1oSwBYUGo3uF6/T0dOTl5UlcDRGRcTCgmCC2oNBAyOVyvPjiiwCAHTt2oLq6WuKKiIiGjgHFhHCYMQ2Wh4cHkpKSAAB79uyRuBoioqFjQDEhvMRDQxEfHw8fHx+Ulpaira1N6nKIiIaEAcUEsQWFBmvJkiUAgHfeeUfaQoiIhogBxYSwBYWGauzYsfD29oZGo0FGRobU5RARDRoDigliCwoNxbPPPgsA2LdvHzo7OyWuhohocBhQTAhbUMgYFAqFYT6U48ePS1wNEdHgMKCYILag0FAlJCQAAE6ePImWlhaJqyEiGjjOJGtCOMyYjMnLyws3btzA73//ezg7O8PX1xc+Pj7w9fWFr68v7OzspC6RiOiuGFBMSHcw4aUeMoYf/ehHqKurQ2VlJSoqKlBRUYFTp05Bo9EAAFxcXHoEFh8fH4YWIjIZDCgmhAGFjEkQBKhUKqhUKoSHhwMA9Ho96uvrUVFRYQguJ0+e7BFabm9pYWghIqkwoBCNIjKZzBBauhcd1Ov1vVpaUlJS0NHRAQBwdXXt1dJia2sr5dsgolGAAcWEsOWEpCCTyeDu7g53d/deoeX2lpYTJ070CC13trQwtBCRMTGgEFEvt4eWiIgIAF2hpba2tkdLy5UrVwyhxc3NrVdLi42NjZRvg4jMGAOKCeIoHjJFMpkMHh4e8PDw6BVaugNLZWUljh07Zpggzs3NrVdLC0MLEfUHA4oJ4SUeMje3h5bIyEgAXaHl5s2bPVpaLl26ZAgtKpWqR0uLt7c3QwsR9cKAQkRGJZPJ4OnpCU9Pz16h5faWlosXL0Kr1QLoCi13trQolUop3wYRSYwBxQTxEg9ZmttDS1RUFABAp9P1amkpKCgwhBZ3d/deLS0MLUSjBwOKCeElHhpN5HI5vLy84OXl1Su03N7Scmdoub2lhaGFyHIxoBCRybg9tERHRwPoCi01NTU9WlrOnz8PnU4HAPDw8OjV0mJtbS3l2yAiI2BAMSF6vR5AV3M4EXWRy+Xw9vaGt7d3r9Bye0tLd2gRBKHPlhaGFiLzwoBiQrhYIFH/3B5aYmJiAHSFlurq6h4tLfn5+YbQcmdLy5gxY/jHAJEJY0AxIQwoRIMnl8vh4+MDHx8fQ2jRarW9Wlq6Q4urqysSEhIQHR3NYc5EJogBxYQwoBAZl0KhMISW2NhYAF2hpby8HJmZmUhOTsbRo0cRFRWFqVOnwt3dXeKKiagbA4oJ6Q4obHYmGj4KhQJjx47F2LFj8cADDyAzMxOZmZnIyMjAhAkTMHXqVAQFBfHfIZHEGFBMCFtQiEaWk5MTFixYgNmzZ+P8+fNIS0vDp59+Cjc3N0ydOhVRUVEcxkwkEQYUE1BcXIzU1FRcv34dgiAwoBCNMCsrK0RFRSEyMhKlpaVIS0vDwYMHceTIEURHRyMhIQEqlUrqMolGFQYUidXW1uLjjz+Gh4cHEhISEBQUBLlcLnVZRKOSIAgICAhAQEAAGhsbkZGRgaysLKSlpWHixImYNm0axo8fzz8iiEbAkC6yvv322xAEAS+//HKP7WfOnMGCBQtgb28PJycnzJkzB21tbUN5KYuVlpYGpVKJZ599FvPnz0dAQIDUJRERAGdnZyxcuBCvvvoqVqxYAbVajU8++QSbN29GRkaGYd6izs5OtLa2GiaOIyLjGHQLSkZGBt577z3Dsuvdzpw5gyVLlmDTpk3461//CoVCgbNnz476DmeXLl1CXl4eOjo6EBwcjMmTJ0OpVKK4uBh+fn6wsrKSukQi6oOVlRViYmIQHR2N69evIzU1Ffv27cO+ffsQEBCAkpISw75RUVGQy+WQy+WIjY2Fg4MD7O3tex1TFEV0dnb2mDzu2rVr6OjogIeHB9zc3EbkvRGZMkEcxAIwzc3NiImJwf/+7//izTffRFRUFN555x0AwLRp0/DAAw/gP//zPwdVkFqthrOzMxobG+Hk5DSoY5ianJwcfPPNN/Dx8YGNjQ2Ki4shiiKsra3R0dGBRx55BJMnT5a6TCLqp9LSUuTm5iIrK6vXY15eXrhx44bhvqurK2bPnm2Ym0UURezYsQP5+fkICgpCfX09dDodGhsbDc959tln4efnN/xvhMiIjP35PagWlBdffBFJSUlYuHAh3nzzTcP26upqpKWlYd26dZgxYwaKiooQEhKC3/72t5g1a1afx9JoNNBoNIb7arV6MCWZrNbWVhw5cgQhISFYu3YtBEFAY2MjCgsL0dDQAJVKhbCwMKnLJKIB8Pf3h7+/P5YsWQKFQoGSkhK0t7cjKCgICoUCV69exdGjRxESEoKUlBTs3r0bzs7OCAwMRG5uLvLz8wF0dZCfMmUKbG1t4eLigkmTJuGdd97Btm3b4OTkhPb2dkRFRSE2NtbQEqNWqyGTyeDg4CDlKSAadgMOKNu2bUN2djYyMjJ6PXb16lUAwK9//Wv8z//8D6KiovDxxx8jMTER+fn5mDhxYq/nvPXWW3jjjTcGUbrpKSkpwalTp2BlZQV/f38EBARg9+7daG5uxsKFCw0d65ydnQ2TRhGR+eq+NDt27Nge28ePH4/x48cD6GpB+eqrr/DJJ5/02OeXv/yloSX1dg899BD27NmD5uZmAMDRo0dx/PhxTJ06FTqdDpmZmdDpdJg8eTLmzZsHDw+P4Xp7RJIa0CWe0tJSxMXFITk52dD3ZN68eYZLPKdPn8bMmTOxadMm/O53vzM8LyIiAklJSXjrrbd6HbOvFhR/f3+zu8STnZ2NPXv2wNPTE1ZWVqioqDB0oouPj0dSUpLEFRKRFHQ6HQoKCtDZ2YnGxka4uLggICDgnv1MNBoNBEGAQqFAZWUlvvnmG1RXV8PJyQmBgYGwt7dHQUEBGhoaEBMTg4SEBHh7ewPoWnS0tLQUtra28PT0HKm3SWT0SzwDCihff/01Vq1a1WMYbPdCXDKZDJcuXcKECRPwySefYP369YZ91q5dC4VCgU8//fS+r2FufVA0Gg0uXbqEnTt3YuzYsXjqqacgl8vR2dmJ8vJyaDQaTJw4cdR3EiaiwRNFEfX19XBycoJC0dXw3dnZiQMHDiA7OxtAV98XLy8vXL9+HY2NjVAoFHj66aehVqtx/fp1BAQEYNKkSeyQT8NG0j4oiYmJyMvL67Ftw4YNCAkJwWuvvYbx48fD19cXly5d6rHP5cuXsXTp0iEXawrq6+tx8+ZNNDY24ty5c6isrERnZye8vLwwa9YsQ3izsrJCYGCgtMUSkUUQBKFXi4uVlRWWL18OBwcHdHZ2Qq1W4+bNmwgODoarqyuOHj2KLVu2GJ6fnp4OoGsgQ2RkJHx8fEb8fRANxIACiqOjI8LDw3tss7e3h0qlMmz/13/9V7z++uuIjIxEVFQUPvroI1y8eBHbt283XtUSaGtrw+nTp5GSkmLYFhQUhLlz52LKlClwdnaWsDoiGo0EQcCCBQv6fCwuLg51dXVQKBRwcnJCbW0tDh06hNTUVKSmpmLChAkICgrC+PHj4eXlNcKVE92f0WeSffnll9He3o5XXnkFdXV1iIyMRHJyMoKCgoz9UiOmoqICn376KTo7OxEUFISwsDCEhYXB1tZW6tKIiPpkbW1t6JcCAD4+PvjBD34AjUaDPXv2ID8/H4WFhVAoFHjllVf6nK+FSEqDmgdlOJlSH5TKykqcOXMG586dg0KhwAsvvMD1OIjIIuj1erz11lvw8/PD+vXrucQGDZlJzIMyGpSXl+Ojjz6CnZ0dEhMTER8fDxsbG6nLIiIyCplMBnd3d9TX1yM9PR3R0dH8HUcmhUNL+tDU1ITPPvsMnp6eeOGFFzB79mz+wyUiizN9+nS0trbi0KFDZt9PkCwPA0ofsrKy0NLSgsceewxKpVLqcoiIhsW4cePg4uJi+J7IlPASzx3Onj2L1NRUBAQEcCppIrJYZWVl+Oqrr6DT6fDiiy9yRloyOQwotykpKcGuXbswadIkzvxKRBYrJycHe/fuhZeXF9auXctpEsgk8RLPbbov50yZMkXyEURERMMlOTkZOp0OK1asYDghk8WAcpuioiIAQEdHh8SVEBENn0WLFsHKygp///vfceHCBcN2nU6HqqoqFBUV4fTp0zh06BCampokrJRGM17iuaWmpgbJycmYMWMGoqOjpS6HiGjYREVFYfLkydiyZQt27NiBrKwsuLu7o6ioCDdv3gQAKBQKaLVatLS0YPXq1RJXTKMRA8ot58+fh0KhwIIFCyAIgtTlEBENKysrK2zYsAGZmZm4du0aioqKYG9vj5iYGEyaNAkuLi74z//8T5w7dw5TpkzBxIkTodFooFAoDAvEEg2nUT+TrCiKOHHiBI4dO4aoqCisXLly2F+TiMgcpKWl4eTJk2hqaoJSqYRGowHQtQZQQEAA/P39ERsbC1dXV4krJVNg7M/vURtQdDodSktLcfToUZSUlGDOnDmYO3cup3smIrqNKIo4f/48GhoaYGdnB41Gg4qKCjQ3N+PatWsAgNdff50tz8Sp7odKFEVkZGTg2LFjaG1thUqlwvr16zFhwgSpSyMiMjmCIPRaxb7b2bNnsWvXLhw4cADz58/nAqpkVKMuoBQVFWH//v0ICAjA3LlzMW7cOF5LJSIahIiICLS2tuLo0aPIyMiAr68v5s+fzz/4yChG3SfzlStX4ODggA0bNiAoKIjhhIhokARBwPTp0/HTn/4USUlJKC8vx9atW9He3i51aWQBRt2nc0lJCcaNG8frpURERuLk5IS4uDg88cQTkMlk+Pvf/476+nqpyyIzN6oCilarRXV1Nfz8/KQuhYjIotTV1aGsrAwymQwNDQ24ePGi1CWRmRtVfVAuX74MnU7HVTuJiIxs586dKCsrQ0hICMLCwhASEiJ1SWTmRlVAKSgogKurKzw9PaUuhYjIolhZWUEmkyE+Ph5BQUFSl0MWYFRd4mlvb+cigEREw2D16tUYO3Ystm/fjvLycqnLIQswqgKKSqVCdXU19Hq91KUQEVkUR0dHPPLII3B0dMT777+PrKwsqUsiMzeqAoqVlRUEQeAIHiKiYWBnZ4cNGzbA1dWVAYWGbFQFFEEQoNPpGFCIiIaJRqNBS0sL3N3dpS6FzNyo6iSrUqmg0WiwZcsW+Pj4wMPDA66urmhsbERxcTHUajUmTZqEqVOnck0eIqJ+amhowO7du+Hu7o6WlhZ0dHQgMTFR6rLIzI2qgBIREQGga7jx1atXkZmZCb1eD0EQ4OvrC3t7exw+fBg5OTl44IEHEBwcLHHFRESmr7i4GFevXsXVq1cBAJMmTYKzs7PEVZG5G7WrGQNdKxo3NTXBxsYGNjY2AIDy8nIkJyejuLgY8+fPx9y5c4e1BiIic6fVanHs2DGcPHkSMTExWLFihdQlkQSM/fk9qvqg3Ekul8PFxcUQTgBgzJgxePrpp+Hp6YmqqioJqyMiMg8KhQILFy5EdHQ0Lly4IHU5ZCFGdUC5G0EQYG9vj5qaGqjVaqnLISIyC4GBgWhvb8eNGzekLoUsAAPKXSxZsgQajQYffvghV+YkIuqHiRMnAgBOnjwpcSVkCRhQ7sLLywsbNmxAS0sL9u3bJ3U5REQm7+bNmwCAsWPHSlwJWQIGlHtwc3PD0qVLkZeXh8OHD6OxsVHqkoiITFJDQwN27doFJycnREZGSl0OWYBRNcx4MKKiotDY2IiUlBScPn0aCxcuxKxZs6Qui4jIJOh0Ohw7dgwpKSkAgKeffhpWVlYSVzU43SM7u905qWdfk3z2ta2lpQU2NjZQKBSGxwVBgFwuh1KpNHLVlosBpR/mzp2LqVOn4sCBAzh+/DimT5/OidyIaNTTaDTYunUrSktLMXv2bEybNg329vZSlzUonZ2d+Oc//4mKiophfZ3x48cjISEBwcHBkMl4EeNeGFD6ycbGBh4eHpDJZPyhIiICcO3aNZSWlmLNmjWYMmWK1OUMyeHDh3Hjxg2sWbMGtra2hu19TRV257bb7+t0OhQWFsLX1xcODg6Gx0RRRGtrK3JycrBt2zY4OzsjLi4OMTExZhvqhhsDygB0dHSgs7MTer2eLShENOr5+PhALpejuLjYrAPK+fPnkZGRgaSkJKO8j7CwsLs+FhcXh/LycmRkZODYsWM4duwYJk+ejISEBIwZM4Zrxd2GAaWfampqcO7cOTg7O7MFhYgIXa0COp0O5eXl991Xr9ejpKQE9vb28PDwGIHq+qeurg67d+9GWFgY4uLiRuQ1x4wZgzFjxmDRokXIyclBRkYGzp07Bx8fHyQkJCA8PNxs+/EYEwNKP9y8eRP//Oc/YW9vj0ceeYQJl4hGvfb2dmzfvh0AsGDBgnvuW1ZWhv3796OiogKCIGDBggWYPXv2SJR5T1qtFtu3b4ednR1WrFgx4r/b7ezsMHPmTEyfPh2FhYVIT0/HN998g8OHDyMmJgZxcXFwdXUd0ZpMCQPKfRQVFWHXrl0AgA0bNsDOzk7iioiIpHfo0CGUlZXB3d39ngurXrx4EV9++SU8PT2xbt06nD9/HkeOHEF9fb3ka/Z8++23qKqqwsaNG3sseTLSZDIZgoODERwcjNraWmRkZCAzMxOnT59GcHAwEhISMH78+FH3xzEDyj00Nzdj69at8PX1xerVqxlOiIjQNYw2JycHQFcL87lz53Dx4kV0dHRAo9HAwcEB9vb2aGhoQGFhIUJCQvDII49ALpdjwoQJUCqVSEtLQ0REBAIDAyV5Dzk5OUhNTcWSJUswZswYSWroi0qlwpIlS7BgwQKcO3cO6enp+OSTT6BSqZCQkIDIyEhJw9RIYkC5h+5rgBEREVCpVBJXQ0RkGmxtbREbG4sbN26grKwMO3fuhLW1NcaNGwcXFxfU19ejuroaer0ey5cvR3R0tKHvniAImD9/PtLS0lBcXDziAUWn0yE5ORmpqamIjo7G1KlTR/T1+8va2hpxcXGIjY3F9evXkZ6ejoMHD+LIkSOIjIxEfHw8PD09pS5zWDGg3MPly5chiiIn1iEiuo1MJsPy5csBdA0gKC0tRXh4OKytrfv1/H379kEul9/z0tBwaGlpwVdffYXr169jyZIlmDp1qslfNhEEAYGBgQgMDIRarUZmZiaysrKQkZGBcePGGeZUscSRpYLY1yBvCanVajg7O6OxsRFOTk6S1KDVanH06FGcPn0aU6ZMwapVqzhyh4jICNLS0nDgwAEkJiaOaEfZ8vJyfPnll+js7MQjjzyCcePGjdhrG5tWq8WFCxeQnp6OsrIyk5lTxdif3wwodygoKMCxY8dQU1ODhQsXYvr06SafsImIzEFGRgb27duHqVOnYvHixSPyh58oisjKysKBAwfg5eWFRx99FC4uLsP+uiOloqIC6enpyMvLAwCEh4cb5lQZaQwow6i1tRW///3v4erqiocffhi+vr4j+vpERJaqvb0df/rTnxAWFoaVK1eOyGtqtVrs378f2dnZiI+Px+LFi6FQWGbPhu6OyxkZGWhsbMSYMWMwdepUhIWFjdh7Nvbnt2X+nxokpVIJZ2dnODs7M5wQERlRSkoKNBoN5syZMyKv19TUhC+++AKVlZVYuXIloqOjR+R1pWJvb49Zs2ZhxowZuHz5MtLS0rBz504cPnwYsbGxiIuLg6Ojo9RlDggDym3kcjns7OzQ2toqdSlERBajo6MDp06dgq2t7YhMPFZaWoovvvgCgiBgw4YN8PPzG/bXNBUymQwhISEICQlBdXU10tPTcfr0aaSkpCAsLAxTp06Fn5+fWXRdYEC5Q11d3YhNd0xENBocO3YMAPDwww8P6+vcvHkTaWlpyM7Oho+PD9auXWt2rQbG5OnpiQcffBCJiYnIzc1Feno6tmzZAmtrazz33HMmteRAXxhQ7qBSqVBTUyN1GUREFqG9vR2nT5+Go6MjAgICjH58URRx9epVpKam4sqVK7C3t8ecOXMwc+ZMi+1vMlC2traYPn06XF1dsW3bNnR0dKClpYUBxZxotVp0dnairq5O6lKIiCxCfn4+AGDZsmVGXQBPr9cjNzcXqampqK6uhpeXF1auXMmF9vrQ2NiIgwcPoqCgAEFBQUhKSoKbm5vUZd0XA8ptLly4gOrqajz99NNSl0JEZPbq6+uxd+9eeHt7IygoyKjHzsjIwIEDBxAcHIwlS5Zg3LhxZtGvYiTp9Xqkp6fj6NGjsLKywpo1axAeHm4254kB5TbNzc0AYDb/84iITJmtrS3kcjmmTJnS71lm++vSpUsICgrCE088YdTjWory8nLs3bsXlZWViIuLQ2JiImxtbaUua0A4PeptoqOj4e3tjb1790pdChGR2bOxsYGPjw+Sk5NRWFhotONqNBpcv34dEydONNoxLUV7ezv279+Pf/zjH9Dr9Xj22Wfx4IMPml04AdiC0oOtrS3i4uKwd+9edHZ28jomEdEQrVmzBnv37sXWrVsxadIkzJgxA2PHjh3SMYuLi6HT6TBhwgQjVWn+RFHEhQsXcPDgQbS3t+OBBx7A1KlTzXqNHgaUO3Q3Q3Z0dDCgEBENkaurK9atW4fc3FycOXMGH330EdavX4/x48cP+phXrlyBq6srV5m/pb6+Hvv378eVK1cwadIkLF261CKm8+clnjt4eXkBAMrKyiSuhIjIMshkMsTExOBHP/oRrK2tceTIEeh0ukEdSxRFFBYWYuLEiaO+v6Ber0dqaio2b96MGzduYO3atXj88cctIpwAbEHpRalUAgByc3MxadIkiashIrIccrkcK1euxFdffYW//e1viIqKgru7OwICAmBnZ9evY9y8eRMNDQ2j/vJOfX09vv76a1y/fh0JCQlITEw0fH5ZCgaUW24fjuXg4DCiy4ATEY0WoaGhWLZsGTIzM3HixAl0dHRAEAS4uLhALpfDzc0NKpWqx83R0dHQWlJYWAi5XI7AwEBp34hERFFEdnY2Dh06BFtbWzz99NMYN26c1GUNCwYUdK0C+dlnn6G8vBzx8fFITEyEjY2N1GUREVmkuLg4xMXFQRRFNDY24urVq6iuroZer0d9fT0uXbqE+vp6iKIIALCysjIElxs3biAwMNDow5bNgVarxVdffYVLly4hOjoaixcvtujPKgYUADt37kRDQwM2btwIf39/qcshIjJ51/NyUXw2GwAgyGQQBAFKO3vYObvAa/wEqMb4Q3afESTdLScxMTG9HtPpdKivr0dtbS3q6upQW1uL2tpa6HQ6REVFDcdbMml6vR67du1CYWEhHnvsMYSEhEhd0rAbUkB5++23sWnTJrz00kt45513AADz5s3D8ePHe+z3/PPP4+9///tQXmpY1dXVwdPTE56enlKXQkRkFlJ3bkNV4RU4qlQQRRGiXo/2lmZoWloAADFLV2D+D3446OPL5XK4u7vD3d3dWCWbLVEUceDAAVy4cAGPPPLIqAgnwBACSkZGBt577z1ERET0euy5557Db37zG8P9/nZ+ksrixYuxfft2bN68GatWrbLY63lERMZSdqFrjZ3gabMhyGSYsmARnNw9oGltwXsv/AAd7W2S1NVwowq5h/fBM3A8wmbPl6QGYztx4gQyMjLw4IMPIiwsTOpyRsyghhk3Nzdj3bp1eP/99+Hq6trrcTs7O3h7extuTk5OQy50OIWEhOAnP/kJHB0dsXXrVsOU90RE1Dev8V2jaApOHkPqjs9xYusHAAClnT3cfMfc9/LOcOhoa8WWf3kWWXt34cC7f8Af1j6IsovnR7wOY8rKysJ3332H+fPnIy4uTupyRtSgAsqLL76IpKQkLFy4sM/HP/30U7i7uyM8PBybNm1Ca2vrkIocCS4uLoZmM5mM08MQEd3L+rfewc++2IvVm34NALBx/P4PUZ1WC7li5Ce6PLD5T722ffv+5hGvw1gKCgqwd+9exMfHY86cOVKXM+IGfIln27ZtyM7ORkZGRp+PP/HEExg7dix8fX1x7tw5vPbaa7h06RJ27tzZ5/4ajQYajcZwX61WD7Qko7l27RrGjRtn8pekiIik0HLmDBq+2t51RxCg8PZC+uH9COrsgGvWWeScfREQAee8XDg0adA0diIcFywYsfpWvLoJl1JPoub6NXS0tUKusMLYiOgRe31jKi4uxvbt2xEaGoqlS5eOyknpBhRQSktL8dJLLyE5OfmuQ5t++MPvO0VNmTIFPj4+SExMRFFRUZ/Lbb/11lt44403Blj28JDL5aPyh4CIqD8av/4azSdPwiYsDO3nzkFQKjG2pRl6vQ5CyinDfuMAWJ9OQ9XlohENKIJMhpAZcxAyw7xbG6qqqvD5558jICAAq1evHrWt+gMKKFlZWaiuru4xJEyn0+HEiRN49913odFoei1MNHXqVABdk+v0FVA2bdqEV1991XBfrVZLNtTXzs4Oly5dQmNjI5ydnSWpgYjIVOlb22AbEYGAf7x/z/2qSy+h9D9+CdvalhGqzHLU19dj69atcHNzw9q1a6FQjN7ZQAb0zhMTE5GXl9dj24YNGxASEoLXXnutz1UTc3NzAQA+Pj59HlOpVJrM9Lzz58/HtWvXsG/fPjzxxBNSl0NEZFL0bW2Q2dr2+VhHWyu+ffunsPsuE17VHbADkDnbC6NnzMnQNTc345NPPoGVlRXWrVtn0ZOw9ceAAoqjoyPCw8N7bLO3t4dKpUJ4eDiKiorw2WefYdmyZVCpVDh37hxeeeUVzJkzp8/hyKbGxcUF8fHxSElJgSiKvNxDRHSblpMnAQDl//pzCNZW8HjxRVj5+qKluR7fPbMcY/NrcX1aAIQfzMYOx8tocbLCkxLXbC40Gg0+++wzaDQabNy4EQ4ODlKXJDmjXtiytrbGt99+i0WLFiEkJAQ/+9nPsGbNGuzZs8eYLzOs2traoFQqGU6IiO4gV6kAANrKSjTu2ImWM2cAAGdeegb+F+qh+e3LePCDQ5j7zL/D1qvvVnPqTavV4osvvsDNmzexfv16uLm5SV2SSRjyxa1jx44Zvvf39+81i6w5KS8vx9mzZ/vsK0NENNoFnzpp+L4gfAoa9p5H8+lWhHi8CnG5FjUTew4tFiGOdIlmR6/XG1YlXr9+/V27Q4xGo7f3zR1EUcTHH38Me3v7UTnenIhoIFQbN0Ld4olm56todz6KRr8TkJXboXNSOqys+u6nQj2JoohDhw4hPz8fjz76KGcxvwMDym2cnJxga2sL1a1mTCIi6pvnKy8j72jP1uYmKKBQdHXs5GXy+8vMzERaWhqSkpJG1RT2/TU6B1f3QRAEBAUFoaysDDqdTupyiIjMRp3OAS06ARPC3+4RTESRl3juRqfTISUlBREREYiPj5e6HJPEgHKbtrY2eHl53XfcecWVemz+0VEc//zSCFVGRGR6RP17SDnxJPbmRcEv4kuEjVksdUlm4+LFi1Cr1ZgxY4bUpZgsBpTbODg4oL6+/r77Hf/8MgAg/3g52ls6h7ssIiKT0anvRGF9IV499ipeLXsVyWOSMTtxNmK8YnrsJ4CXeO4lNTUVgYGB8Pb2lroUk8U+KLdotVrk5+cjMDDwvvs+9h8JKLtUj7amDtjYj/yCWEREI0kv6nGu5hyOlh7FP/P/adju7eCNxWMX45mIZySszvyUl5ejtLQUa9eulboUk8aAckteXh7UajXWrVt3330FQYB/CMepE5HlO3/zPH569KeoaauBm40bIj0ioZQr8cd5f4Sz8t5LgnCYcd9SU1Ph4uKCSZMmSV2KSWNAuaWwsBAeHh7w9PSUuhQiIpORXpWO5s5mfLTkI0R6REIu672kCfWfWq3G+fPn8cADD4zaRQD7iwEFwOnTp3H+/HksW7asX/sXF/8d14r/irEBP7zrPnKFHfz9noJMdmudoZxPgcYyQBAACIBMBkQ8BjiPMcI7ICIyDq1ei1+d+hVutt2ECBEl6hKMcRjTq4/J/bAPSt8yMzOhUCgQHR0tdSkmb9QHlPb2dhw9ehTTpk1DQkJCv55TVr4Ven07Kiq/7PNxjaYKVi3ecLKKgatPLNDZDnzzY0DpDFjZAKIItFR37Tz7Z8Z6K0REQ3al/gr2XN0DW4Ut5vrNhauHK2b5zRrUsTjMuKfOzk5kZmYiOjp61C8E2B+jPqBUVVVBq9UiICCg38/x93sKxdf/hlkzT/X5eP35fLR8Uo+WU61weVMPQdR3PZD0ByDika7v/2scwL8wiMjEyISuyw5t2jbUttdCo9PgP079BxysHLAgYIHE1Zm3vLw8tLa29vuP4dFu1AcUb29veHt7Y8eOHbh48SJmzJhx32Ff2gP5cPu0DUWP/wsEpTUUdq7Qt7dC19IEuasLmora4IoVAIAf//l5eNtV4/U7DyLqAF7LJSITM9F1ItaHrkddex0AYP+1/QCAooYiBpQhEEURqampCA4O5mzl/TTqA4qNjQ2eeeYZpKWlISsrC+fOnUNgYCASEhIQGhra53TN2i9zoKgRoPlrMgQR0NzaLsoAQQ/UunvCfvoiCIIM+uYqbHcvxutOfoD7xO8PotcDAgMKEZkWmSDDawmvAej6UK1urUbmjUw8FvLYgI7Dqe57Ki4uRnV1NZYsWSJ1KWZj1AcUALC2tsbs2bMxY8YM5OfnIzs7G19++SXCwsKQlJQEe3v7Hvu7P7URNX/5K0KyMiF2dKCjpRZyKzsoHJzRVJiJgMpKvLf5L10dzGKr8V74T4DY53u+KFtQiMjEna89j8wbmfifuf8DR2tHqcsxa6mpqfD09OSCgAPAgHIbuVyOyMhIREZGIj8/H/v27cPmzZuxcuXKHuPVBUGA2NqK+k+2olrTiatt7VAIAuQAFIIAl2vZWHq2EI6L1VivnAaHU2fQcjrttlcSYNfZDn1bOxhRiMhU1bbVAgCCnIPusyfdS11dHS5duoQVK1awZWkAGFDuIjw8HIGBgdi9ezc+//xzPPDAA5gxYwYEQYDV2LGAIKD6j39Ehyjizh4relGEXKmDl3Ub5DVH+jy+XgRaLlTCaf7wvxciosEoaiwCAPw5588AumaU1el10Ik6iBChEBSQCTLIZfIe38sFOXKrc+Fpx3mlACAtLQ22traYMmWK1KWYFQaUe3BwcMBjjz2G7777DsnJyaisrMS8efPgPm8eQgsuAAA25F1Dh17E1ohx6BBFdOhFZBVcwdKb7djlIGJ6fN9j3S/PmAm3pyeM5NshIhqQeK94zBwzE536TsiFruBhrbCGTJBBEAToRT20ei30oh5t+jbo9XroxK4A427rjvn+/AtMo9EgJycHU6dOhZUVl0YZCAaU+5DJZEhMTIRKpcK3336Ld999F2FhYVixYgVsbGwgQoQgdF32UQoClDLA7tbkgPf9UeQcAURkwqZ4TMHfF/5d6jLMWm5uLjo7OxEXFyd1KWaH8+z2U1RUFF5++WWsWLECRUVF2L17NwBARO/ZTDpv5Q6re11q5HVIIiKLptfrkZ6ejrCwMDg733vdIuqNAWUAFAoFYmJiEBISgrKyMuh0OujF3gFF273/fY/IFhQiooFo0urMZobaq1evora2lhOzDRIv8QyClZUV1Go1/ud//geigxtqHFxQFuSNY41taNXpkFbTCECJ7K93oSkvF3K5HEL3PyhRhAABjU5OcNDrJX0fRETmJPAX+wAAHXEqVDw8TeJq7i8tLQ3e3t4DmqmcvseAMghLly5FSEgICgoK0JaVhQk15Xjjn3XYFxoPvUwGQAmFTosLSmsUXb/e90GmJkCvUMB3RCsnIjJRLbVA1gddk1jKZF0TWdq6ADFPG+aMSpzshSPnb+ClyX7S1toPtbW1uHLlClauXMmhxYPEgDIIcrkcEyZMwIQJE5CYmIiC66XY/cXnWFl6EY+EToQt9Nh/4ACWdUTBS++M/Y2dsLaVY92vYrsOIAjY/N57sLo1t8qJshNIKUtBgFMAojyiMMWDQ9GIaJRJ3Qyk/AFw8AJEPaDtADSNQMB0wDMUALDlSfPpaJqeng5bW1uEh4dLXYrZYkAZIjs7O8SGTkKmjw+E4ss4UXwZACAHcMg6Fwq5Ao4qN0xbMA/fHj+Ozs5OWFlZoaOjA1VVVSgsLMTLJ15Gp7wTABDiFoKvln8l4TsiIpJAaTrgNAZ4tWsKB31JKr75cjVai/fDrvEilgcth0JmHh9ZGo0Gubm5iI+P59DiITCP/9tm4OGHH8b169cxceJEtLS04Pjx4+jo6EBtbS3qG6qxc8+XvZ5TUFCAgoICrBRW4orjFeS55sFG3nsJblEndg1JlgmAwDUuiMgCFaf0uHulqRS/8lABBR8CAMLdwzHRdWIfTzQ9Z8+eRUdHB+Lj46UuxawxoBiJSqUyrFDp6OiItWvXGh6rqalBamoqEhISoFKpoNfrIZN1DaBqaGhAQUEBZMdkWBG8AnPnzu1xXH2HDpVvpUNs036/USYAMgF2kR5weyR4+N8cEdEI0+jaAQDOVg7QQ8AYhzESV9Q/oigiPT0doaGhHFo8RBxmPAI8PDywfPlyeHl5QaFQwNraGgqFAgqFAu7u7pg9ezamTZuGy7mXoWjvmRnFDh3ENi1sI9zhMGsMZPYKyGzkkNtbobOqRaJ3RERkZFHrATt3w12truuyd2NnMx6d9CjsrOykqmxArl69ips3b3JosREwoJiIuXPnQqfTobCwsOcDt0Yn20V7QrCSQd+ihb5VC12jBoKMl3qIyEI0XAdabwLZnwDZn6CzNNXw0HX1dVQ2V0pYXP8dO3YM1tbWGDt2rNSlmD1e4jER1tbWcHJyQmFhIeLj4w2XgAxzuQkCHKb5oOlYKeyiPWEzyRUKT3vJ6iUiMiqnW5Mu7P4JAEBrawN4e8LHzgvflnwLeyt7vDnrTQkL7J/S0lIA7CtoDGxBMSHLli1DYWEhduzYgfb2ruuvhvV6BKAlowoQAbsoT9hFesLahwGFiCzEqveAX9XdutXjzIL/BwCobL0BAIj27HvhVVPS0tJ12X3BggUSV2IZGFBMyKRJk/Dwww+jsLAQ7777LjIyMqDXds02K3booWvuuiYr6sWukT1ERJZCELomZJPJAZkMMV6xPR5eFLhIosL6LysrCwqFggsDGgkv8ZiYyZMnw9fXF8eOHcO+fftwNfAKIgR3OJSo4bx0HFrP1uDaRxn4UnkGD614CFExUVKXTERkdNZyawDA6gmr8cbMNySu5v50Oh0yMjIQEREBOzvz6NBr6hhQTJCrqytWrVqF4OBg7Nm9GwXKy7BLz4TnRXeobBxQpb0JAPh699cMKERkkS7Xd016eU19TeJK+qegoABNTU0cvWNEgmhiy0Kq1Wo4OzujsbERTk5OUpcjufb2dhQcyUH5tTLcqK9GjbYB7ULXpR4bGxtERkYiIiICPj4+33esJSKyAB26DljJrMyiw+mWLVsgl8vxgx/8QOpSJGPsz2+2oJg4GxsbRCdNR3f3MF1bJ5rbWlBVcwPFxcXIzc1FWloaXFxcMH78eKhUKsTHx8Pa2lrSuomIhqr7Mo+pKy8vR2lpaY8JOmnoGFDMjNzWCs62LnB2c8GkSZOwcOFClJSU4OzZs6isrERubi7y8vKwePFiBAYGmsVfHkRE5iw9PR3Ozs6YdGsBWDIOBhQzJ5fLMW7cOIwbNw4AUFVVhZ07d+Kjjz6Cg4MDYmNjMXnyZLi7u/MSEBGRkTU3NyM/Px8LFizg71gjY0CxMN7e3njhhRdQXFyMCxcu4NSpUzh+/Djc3NywZs0ajBljHutZEBGZg6ysLMhkMsTExEhdisVhQLFAgiAYWlUWLlyIsrIyHD16FB999BHWrl2LoKAgqUskIjJ7Wq3WMLTY1tZW6nIsDtujLJxSqURQUBCeeuop+Pn54ZNPPsEXX3wBnU7XY7/s7Gx88sknqKmpkahSIiLzUlBQgObmZkydOlXqUiwSW1BGCaVSiXXr1uHcuXPYvXs3MjIyIJPJcOjQoR5hpby8HB4eHhJWSkRkHtLS0jBu3Dh4enpKXYpFYkAZReRyOaKjo5Geno7s7GxUV1cD6JoYzsfHBxcuXMCZM2dQUFAAe3t7JCUlQS6XS1w1EZHpKS8vR1lZGR577DGpS7FYDCijzLFjx1BZ+f2y5atWrUJkZCREUcThw4dRV1eHlpYWXLp0Ca6urpg9e7aE1RIRmabu+aeCg4OlLsVisQ/KKOPu7t7jvr+/P4CujrWLFy/G448/jnnz5gEAjhw5MtLlERGZvO6hxQkJCRxaPIx4ZkeZ8PBw/Pu//7uhn0lnZ2evfbpnoU1KShrR2oiIzEFWVpbhkjkNH17iGYUUCgVWr16N9957Dx9//DGmTp0KQRCQkJAApVIJhaLrx6K+vr7H8/R6LYqK/hudWjUECF3Lo0OAvV0QAgKekeCdEJGlU6vV0Gg0EAThnjcAg95nIDi0eOQwoIxS3Qs5tbS04OjRowC6LuksnjIXMQ/OAACcPn0aM2fOhL29PQCgvb0UJaVbYG8/EXK5PSDqoW46BwDw99/AafWJyKjUajX++Mc/DvvrDCTs6HQ6aDQarlo8AhhQRimlUgm5XN5rPpRDeccxddVcPPDAA0hOTsaFCxcQHx8PACiv2AYACA15G87OUQCAyqqvceHCz1BW9jG0WjV8fddCqeSQOyIaOkdHRwDA+PHjMWfOHIiiaLgB6HG/r1t/9hnofkePHoWNjQ28vLwkOCOjCwPKKKVQKJCUlITdu3fD2toaHR0dAABnW0fs3r0bFy9eBIAerSIlJf8AAFwr/guiIj8AANjZBkIms8XlK78BAChtfODr8/BIvhUislCCIMDR0REBAQEIDAyUuhxkZ2dDFEXExcVJXcqowE6yo1h0dLQhnISFhQEAmjQtOHfuHNrb27Fw4ULDdgBQqeYBkKGl5aphm7NzFObPy4ezc9c6FM5O7DRGRMbR2NiIpqYmuLq6Sl0KAODEiROQyWSGkY40vNiCMooJgoCf//znyMjIwPHjxyGTyZCYmIiZM2f2uX9U5BYUFv0e1Tf293pMoXAGALS0FMLenmv9ENHQpaamQi6Xm8RcI1VVVWhoaEBwcLBhIAENL57lUU6hUGD69OmIjo7Gd999h+TkZDQ1NSE2NrbPKe+bms5DhNhjm06nQW3tdwCA9vayEambiCyXKIrIzMzEmTNnsGjRIpMYLfP3v/8dALB06VKJKxk9eImHAAA2NjZYsmQJ5s6di9zcXGzevBn/+Mc/cPXq1R77NTXlo729tMc2uVyJqMgPAQBXCn+HI0eDUFr64QhVTkSWpqqqCvv27QMARERESFwNUFRUZPjeVC43jQaC2N012USo1Wo4OzujsbHRMBSWRlZnZycuX76M1NRUlJaWQqVSwdPTEyEhIWhv/w90dNYgLPS/bntGV0faq1f/aBh2bGMzBjNnnJCgeiIyd3q9HqmpqUhJSYEoili4cCFiYmIkm7X1gw8+QElJCV555RU4OztLUoM5MPbnNwMK3ZUoirhw4QJKSkpQWVmJkpISzJrlAkH217s+x8trOSYE/RxWVi6Qy+1GsFoisjQtLS1ITk5Gbm4u/P398eCDD0oyvPc3v/kNXF1d8dOf/nTEX9ucMKCQZLZu3Yqqqio8sGgGxo/zh5VVz5WORVGEUukJmcxKogqJyBIVFxdj7969qKurw/Tp0zF37lzDkhzDraSkBB988AFmzpyJBx54YERe01wZ+/ObfVCo36ZPnw65XI5dOw/jgw92orFRDhsbX8PN1nYMwwkRGV1gYCB+9KMfYd68eUhLS8PmzZt79Y8bLrm5uQCAmJiYEXk9+h4DCvVbUFAQXnnlFfz4xz+GQqHAjh070N7eLnVZRDQKKBQKzJkzBz/+8Y/h5uaGjz/+GIcOHYJWqx3W1y0uLoZCoYBKpRrW16HeGFBowDw9PbFmzRo0NjZi8+bNOHHiBFpbW6Uui4hGATc3Nzz55JNYtGgR0tPT8f777+PGjRvD9noNDQ0MJxIZUkB5++23IQgCXn755V6PiaKIpUuXQhAEfP3110N5GTJB3t7e2LhxIwIDA3HixAn86U9/wvnz56Uui4hGAZlMhhkzZuC5556DXq/H//3f/yEjIwPG7lJZVlYGvV6PoCBOPimFQU/UlpGRgffee++uY9Tfeecdrm5r4bpbUpqbm3HgwAHs2LEDN2/eREBAALy9vU1iciUiMg16vR7Nzc3YunUrBEHA6tWr+1wtGLj7SsJ3Pubi4oKnn34aycnJ2LdvH4qLi7F8+XLY2NgYpeZz57qmTYiMjDTK8WhgBhVQmpubsW7dOrz//vt48803ez2em5uLP/zhD8jMzISPj8+QiyTT5uDggNWrV+PgwYNISUkxXBO2sbGBnZ0dHB0dERoaisjISCiVSsnmMiCikaHX66HT6Qy3hoYG7NixA3V1dYZ9/va3vxn9dc+fPw+9Xo+1a9ca5XjXrl2DTCbjysUSGVRAefHFF5GUlISFCxf2Ciitra144oknsHnzZnh7e9/3WBqNBhqNxnBfrVYPpiSSmFwuR1JSEpYsWYLa2lpUVVVBrVajtbUVtbW1OHToEA4ePAiFQgF/f38EBwcjPj6ea1oQWZAbN27g/fff77PjqqurKx5++GFYW1vDxcUFHR0dEEXxrjcA93z8bvv053OnP/R6PWpqatj/REID/nTYtm0bsrOzkZGR0efjr7zyCmbMmIGVK1f263hvvfUW3njjjYGWQSZKLpfD09MTnp6ePbar1WoUFRWhtbUVxcXFSE5ORnl5OR5++GGJKiUiY6uoqIBWq8Xs2bPh7u4OuVwOuVwOhUKBMWPGwM7OfCZvTE9PBwCMGzdO4kpGrwEFlNLSUrz00ktITk7u8xrf7t27cfToUeTk5PT7mJs2bcKrr75quK9Wq+Hv7z+QssgMODk5ITo6GgAwc+ZMZGdnY/fu3fD09MScOXMkro6IjKH49EUAQEpKCqwgByCguyfi999130ffjws973+/nwDZrX4oMgiAIEB2a7sgdO8hwMvFA6t/8sSQ38vFi13vZcmSJUM+Fg3OgAJKVlYWqqure0xYo9PpcOLECbz77rt44YUXUFRUBBcXlx7PW7NmDWbPno1jx471OqZSqYRSqRxU8WS+oqOj0djYiKNHj6KmpgbLli1jp1oiMzcrLAHikXY4jXOHXCmHKAIQ0bUCunhrHXRRRPeC6KIo3noMPR4T8f0lnK6nioZ99aL+1n1AFPW3tnU9XtNUh4Kaoj5rG6gbN27Azs6Ol6ElNKAzn5iYiLy8vB7bNmzYgJCQELz22mtwd3fH888/3+PxKVOm4E9/+hOWL18+9GrJYgiCgHnz5sHNzQ379+9HRUUFHn/8cbi7u0tdGhENkoOPKxK0E+DzyFTInUZmKvrbndh2GMcLUod8nI6ODrS1tWHChAlGqIoGa0ABxdHREeHh4T222dvbQ6VSGbb31UEpICCA1/GoF0EQEBkZCT8/P3z++ef44IMP8NJLL7FFjYgGRS6XQwc99Hr9kEYL5ufnAwBCQ0ONVRoNAsd7kuRUKhUef/xxtLa24siRI9Dr9VKXRERD0nV55mp2BtqaRm5kpkKhAARA1A1twraCggIA6PUHOY2sIV9c66tfye1MbLFkMlEqlQqLFy/GoUOH4OjoiNmzZ0tdEhEN0a7/egNhs+dj6U9+NuhjiHoRovbWHy3irf90f6yI6Orbcuu+7FaXWnVDI6zterbE3m/i0NsfLy8vh42NDVtzJcbeP2Qypk+fDrVajSNHjqC0tNQwZwIRmQdB3vUhX/m7dEAm4NHxP4dQIUPZv528yxP6cdABtIZ0ymoBa+DPm//S7+fcTWBg4JCPQUPDgEImZdGiRfD398fOnTtx5swZzJ07V+qSiKiflOOc4fpIMMTO/lym7WfwEAFRK0LuaNV1X7j1H+H777sbP1xEHVyaxkPm1tXycbcW/Hu17KvVahw8eBDTpk3rX300bBhQyKQIgoCwsDAUFBTgzJkzmDBhAsaMGSN1WUTUD4JCBvtYaaeFD8PQXj8rKwuCIHBghwlgJ1kySUuWLIFKpcJHH32Ey5kXcPPjC2g9Ww1Rq0envlPq8ojIQl2/fh3e3t7sf2ICGFDIJNnb2+Ppp5/uGoK890s0X6hG3eeX8OLfNiDmkxjsLtotdYlEZIGuX7+OsWPHSl0GgQGFTJi1tTUWPrAQIoCTtmdRGvIBwsd1rQEV7REtbXFEZHEaGxvR2NiIgIAAqUshsA8KmThvL2/Y2NuguLIIWy7NxOFr36Bt4jyMseEKo0RkXNevXwcABhQTwRYUMmkymQwzp82E1tEVjyuOw0rQ4WeFRyD7nQ+grpC6PCKyICUlJXB3d4eDg4PUpRAYUMgMJCQkYIyfH2rtwvCBbglqqryQnrsQB/Z/jcZOLYpa29Gs1UldJhGZuevXr7P1xITwEg+ZPKVSiRkzZuCrr75CVfSr2HagGjJve+AY8PGNdHwXYQelTMAzY9zx+gQOSSaigWttbUVNTQ1mzZoldSl0C1tQyCzk5+fD29sb/7Z6KmRye8P2WQXt2B4VhCQPF/yttAatOq7jQ0QDV1JSAoD9T0wJAwqZhZqaGvj7+wMAxq34fgjgxWBbyCBAKeuaSlLen6mziYjuUFJSAicnJ7i4uEhdCt3CSzxk8srKynDz5k3MnDkTABCd6I9nbJsMj3+VWwgAGG+rhHIIS6wT0ehVXV0NHx+f+y4qSCOHAYVM3unTp6FSqTBlyhQAgI/SGudnhqNJp4N4a2FTESLcrfjjTESDU1tbi9DQUKnLoNvwNzqZvMbGRowZMwYKxfc/riprBVT88SUiI9BqtWhoaIBKxfmVTAnbw8mkiaKIGzduwNPTs1/7a/Uinjp3Fc/mX4NW3/9l2olo9Kqrq4MoigwoJoZ/gpJJ0+l00Gq1sLGx6df+fsfPGr5v0engLOOPOBHdW21tLQDA3d1d4krodmxBIZOmUCjg4uKCzMxMiOLAWkSc2SeFiPpBrVZDLpfD3t7+/jvTiGFAIZMXHR2N6upq6HT3ny32s4jxeGmsF9KmsbMbEfVPW1sbbG1tOYLHxPBPTDJ53cP/bu8kezcLVE5YoHIagaqIyFJ0BxQyLWxBIZPn4OCAqqoqnDt3TupSiMgCtba2MqCYILagkMlbuHAh2tvbsXPnTtTW1mL+/PlSl0REFqStrQ12dnZSl0F3YAsKmTwrKys89NBDmD17No4fP476+nqpSyIiC8JLPKaJAYXMgiAIcHR0BIB+9UUhIuovBhTTxIBCZkGn0+HIkSMICwszBBUiImNgQDFNDChkFmQyGRwcHPo11JiIqL/0ej0DioliQCGzIAgCZs2ahUuXLmHfvn3Q6/VSl0REFqC9vR0A2EnWBDGgkNmIiorCzJkzkZGRAbVaLXU5RGQB2traAIAtKCaIAYXMhiAImDBhAgCgs7NT4mqIyBIwoJguBhQyK919ULRarcSVEJElYEAxXQwoZDaampqwa9cueHt7w9vbW+pyiMgCMKCYLgYUMhs1NTVoaWlBdHQ0F/UiIqNobW2FQqGAtbW11KXQHRhQyGwEBgYiPDwchw4dQmFhodTlEJEF4BBj08WAQmZDJpNh1apVCAoKwldffYWWlhapSyIiM8eAYroYUMisyOVyLF26FBqNBqWlpVKXQ0RmjgHFdDGgkNlxcHCAIAicC4WIhowBxXQxoJDZOXLkCABg4sSJEldCROaOAcV0MaCQWRFFETk5OQgPD4erq6vU5RCRmVMqldBoNFKXQX3guvVkVgRBQHR0NNLS0lBcXIyIiAhERkbC09NT6tKIyAy5ubmhrKxM6jKoDwwoZHYWLVqEsWPHori4GFlZWTh16hS8vb0xc+ZMTJ48GTIZGwaJqH/c3Nxw7tw5iKLI+ZVMDAMKmR25XI6wsDCEhYVh0aJFKCwsRGZmJnbs2IETJ07gwQcfxNixY6Uuk4jMgJubGzo6OtDS0gIHBwepy6Hb8E9NMmsKhQIhISFYv349nn32WdjY2GDr1q2orKyUujQiMgNubm4AgLq6OokroTsxoJDF8PPzw5NPPgkPDw989tlnHIZMRPfV3dmeAcX0MKCQRbG2tsbjjz8OQRCwa9cuqcshIhNnZWUFJycnBhQTxIBCFsfR0RHTpk3DtWvX8P4fPsfuv+Si4Uar1GURkYlyc3NjQDFBDChkkSIjIyEXrFDZUIzSC3XY++5ZqUsiIhPFgGKaGFDI4mi1Whw+fBg6sRN2LX4AgMBId4mrIiJT5ebmhtraWoiiKHUpdBsOMyaL0tzcjC+++AIVFRVYvXo1JoeFQ65gDieiu3Nzc4NGo0FbWxvs7OykLoduYUAhi1FUVISdO3dCEARs2LABfn5+fe53NacGB97LM9yXyQR0/90Ul/E2HJtKDI8FZ2ZAzrkRiCza7UONGVBMBwMKWYSOjg7s2rULrq6uWLt2LRwdHe+674kvLhu+t3W0QsKD4wBBQMuvXoZjUwn0Xn6Q3eia+lrs7Bz22olIWrcPNb7bHzY08hhQyCIkJydDo9Fg1apVPcLJZ7/8GSoLLxnuWzvMg8wqBgDwwMYwTIzzgiAIEPV6XKzKBwCofvADyOqqUPv+P6BXqwEuSkhk0ZRKJRwcHNhR1sQwoJDZa25uRmZmJhYuXAiVStXjse5wYm1ri462NnS2nsPMx1cjZLo3nD2+b8otv9yA7KiXEZP7Dur/603DdkHBfyJEowFH8pge/vYls1ZeXo69e/fC1tYWUVFRvR53dPdA080adLS1AQBEUYOwWb5wdLMx7NPe0olv3skFXCbC/j//DEdtLQBA7uICha/vSLwNIpKYm5sbbt68KXUZdBsGFDJbZ8+eNfQ7Wb9+Pezt7Xvt8+xf/4Hsfd/gxrVruJJRC4XtdFgp5YbHWxo02PH7LMP9tEJXPLJp0YjUT0Smw83NDZcvX77/jjRiGFDIbFVUVMDZ2Rk//elPIZP1PZRYJpMjJmkVPvn307Cy1wAAPvnlaWg79QAAva5r/I7PBGdUFjZi5iMTex+koQS4dBCYtBRw8R+eN0NEknJzc0Nrayva2tpga2srdTkEBhQyY46Ojmhvb79rOOmm69SjuU5juN/Rrvv+QVEP38pTCGhtRlT9JWh/54HaWbOgemZD1+MFe4GvfgDoO4G2OmDeL4bhnRCR1LqHGtfX1zOgmAgGFDJbcrkcer3+vvvp9XefHXL8tb0ILDkEANAA0Fy5gpbTp6FaFg+c+D1QsBsImAHcyAf0urseh4jM2+1DjX3Z98wkcIpNMlutra2wsbG5736alq65TKIW+uOp383Aomcnw3u8MwAYwklt4DRo5UoAgFucEvj7TKAyF0j8FfD0bkDpCIj3D0NEZJ5sbW1hZ2fHkTwmhC0oZLY0Gg3kcvl99xNkguGro5sNHN1sMDHOC7nfluCk5neYdebfoCpORY0qAlbTJiFkfgvgFwuEPQRYdw9FFgBwnQ4iS8ahxqZlSC0ob7/9NgRBwMsvv2zY9vzzzyMoKAi2trbw8PDAypUrcfHixaHWSdSLh4cH1Go1tFrtPfezcbACAFw4WYHjn3XNi3IprQqndhRi0uLJwJ+2AwvGY+ZvojH9D7+B8OAfgKgnbgsnuJVPGFCILBkDimkZdAtKRkYG3nvvPURERPTYHhsbi3Xr1iEgIAB1dXX49a9/jUWLFuHatWv9+muXqL88PT2h1+tRW1sLLy+vu+5nZS1H4tOhyD9RjvwT5Wiqb8f1vFpMjPPE7EcndrWwnC8EhBmATgvI5IAg9DjG4YqncL0wEvLDJyEIgCAI33+VARAEJCQFYtI0n2F+10Q0XNzc3HD16lWpy6BbBhVQmpubsW7dOrz//vt48803ezz2wx/+0PB9YGAg3nzzTURGRqK4uBhBQUFDq5boNt7e3lAoFLh8+fI9AwoAhEz3gaZVixvX1Ghp0GDhhjAEJ3RNcw8AZUflaN36EQThIwgysSt0yLrChyATUDbpP6FTAtHzxkAUcesmAiIg6kVcOF2JyqtqBhQiM+bm5obm5mZoNBoolUqpyxn1BhVQXnzxRSQlJWHhwoW9AsrtWlpa8M9//hPjxo2Dv3/f80doNBpoNN8PAVWr1YMpiUYhpVKJ0NBQnD17FrNmzTKEjbuJTPRHxAK/PvdruioCkMN99SyIWi1ErRbQaSFqdRB1WqAdmOBQjrhlfU/iVnKhjpeAiMzc7asa+/jwjw2pDTigbNu2DdnZ2cjIyLjrPv/7v/+Ln//852hpacGkSZOQnJwMa2vrPvd966238MYbbwy0DCIAQFhYGPLy8tDY2AgXF5f77n+vEGM/cyY8fvd+n4+1/ego1Hqrex6b8YTIvDGgmJYBdZItLS3FSy+9hE8//fSewzvXrVuHnJwcHD9+HMHBwXj00UfR3t7e576bNm1CY2Oj4VZaWjqwd0Cjmre3NwCgqKhoSMeRu7vDLi4WW05eQ+Av9uHn28/22qeyxfmuzxc4yIfI7Nna2sLGxoYdZU3EgFpQsrKyUF1djZiYGMM2nU6HEydO4N133zUM+3R2doazszMmTpyIadOmwdXVFbt27cLjjz/e65hKpZLX+mjQXF1dERoaipSUFMTGxg7+QLcaVnycu4J3ypWei4ZZa1sw1r31Hs8XeImHyMwJgsCRPCZkQAElMTEReXl5PbZt2LABISEheO211/ocpSOKIkRR7NHPhMiY7OzshhxyBUGG2i0fYOKXX+Gg2DVLbeF3/911SUgmg9zzKVxXu+Hz36Td8byur7XlLXAf03uxQiIyLwwopmNAAcXR0RHh4eE9ttnb20OlUiE8PBxXr17FF198gUWLFsHDwwNlZWV4++23YWtri2XLlhm1cKJu9vb2UKvVEEXxvh1l78br338JzcVLEEU9oO8epqOHqO+6H9HchJaAIMicb13mEW+7oiMCvhNcEDzN2yjvh4ik4+bmhuvXr0tdBsHIM8na2NggJSUF77zzDurr6+Hl5YU5c+bg9OnT8PT0NOZLERk4OTmhra1taMdYtAhY1PcIHQC49yBmIrIUbm5uaGpqQkdHx10Hd9DIGHJAOXbsmOF7X19f7N+/f6iHJBqQ7pVHP/roI0ybNg0hISESV0RE5ur2VY3vN78SDS8uFkhmLywsDKtWrYJer8e2bduwf//+rknUiIgG6PahxiQtBhQye4IgIDIyEhs2bEBiYiLS09NRW1srdVlEZIbs7e1hbW3NgGICGFDIYgiCYLi8w4BCRIPBocamgwGFLIpKpYK9vT0KCwulLoWIzBQDimlgQCGLIpPJIJfLkZOTg6qqKqnLISIzxIBiGhhQyOIsWbIEWq0WV65ckboUIjJDbm5uaGxshFarlbqUUY0BhSxOWFgY/P39UVlZKXUpRGSGbh9qTNJhQCGLFBgYiOLiYg43JqIB41Bj08CAQhbJz88Pra2tuHHjhtSlEJGZcXBwgEKhYECRGAMKWSR/f38AwO7duyWuhIjMjUwmY0dZE8CAQhbJzs4Obm5uqKiowJkzZ9Da2ip1SURkRhhQpMeAQhbrqaeegr29PQ4dOoRt27bxlw0R9RsDivQYUMhiubi44NVXX8XatWtRUVGBv/zlL/jggw/Q0NAgdWlEZOLc3NzQ0NAAnU4ndSmjFgMKWTS5XI7Q0FD8y7/8C9asWQO1Wo0PP/wQFy9ehF6vl7o8IjJRbm5uEEWRf9BISCF1AUQjwcnJCVOmTIG/vz927NiBbdu2wdXVFX5+ftBqtZg8eTKCg4NhbW0tdalEZAJuH2qsUqkkrmZ0YkChUcXFxQUbN25EaWkpsrOzUVtbC51Oh+3bt0MQBHh6esLX1xdjxoyBr68vvLy8IJfLpS6byKQVVKpxML8KdS0d8HJS4rk546FUmPe/GycnJ8jlcvZDkRADCo1K/v7+hqHIAHDz5k0UFxejoqIC5eXlyM3NhSiKkMvl8Pb2NoQVb29veHt7Q6HgPx2ibmv+dhqtHd/31Xgoegz8XO0krGjoZDIZXFxcGFAkxN+yRADc3d3h7u5uuN/R0YGqqipDYLl27RoyMzN7hJZx48YhISEBTk5OElZOJD0v8SYmya4gz24qIsd5mX046ebm5sbp7iXEgELUB2trawQEBCAgIMCwrbOzEzdu3EBZWRnKy8uRmZmJnJwc/PSnP4WNjY2E1RJJ6zv5i4AcQOQGYPk7UpdjNK6urrh69arUZYxaHMVD1E9WVlbw8/PDtGnTsGbNGjz//PPQaDQ4efKk1KURSevBd7q+xj4taRnG1t2CwhF/0mBAIRokV1dXxMfHIysri7/AaHSL2wD8uhHwjZa6EqNydXWFTqdDU1OT1KWMSgwoREMQFhaGtrY2ZGdnS10KERlZ91Bj9kORBgMK0RD4+/sjJiYGe/fuxalTp6Quh4iMyMXFBQA4kkci7CRLNETLly+HIAj47rvvEBkZCQcHB6lLIiIjsLKygpOTE1tQJMIWFKIhEgQBc+fOhY2NDbZt2wZRFKUuiYiMxNXVlS0oEmFAITICJycnLF++HGVlZaioqJC6HCIyEs6FIh1e4iEykvHjxwMAPv/8cyQmJkIQhD5vAO76mCAIhhYYURTv+v3tX+/8/s5tbW1tsLW1hSAIkMlkhteRyWR93m7fb6Bf79xGZO5cXV1RUFAgdRmjEgMKkZG0t7cDAJqbm/HNN99IXI1pGErYuVvoGexz+7rf/X13rTdv3kRBQQFiYmLg5uYGmUwGuVx+z1t3qNTr9T2+3itUGvOrTqdDfX19nwH49u/v99i9ztP97t/t//1AtvdHfy+f9me//h6rqakJ7e3thqBPI4cBhchIHB0d8frrr/f4cLqzBaQ/jwF9f4h0f3/ntm59/eLvq0Xmzg/Tu936+tC939fBPMcYx9ZqtXfd937f335uWlpaAADnzp2DQqGATqeDXq+HTqfrdW6l0tfPgk6ng4eHR79a3+71M3lnuLp922jW/bNAI4sBhciI7vUXJZmv7g9qnU7XI7R0f3/nJbO7hcrBfjWFn6l7BZm+9r3bMe62vb/vUYr95HI5FwiVAM84EdF9CIJguKQzWt0elEbzeaCRw15sREREZHIYUIiIiMjkMKAQERGRyWFAISIiIpPDgEJEREQmhwGFiIiITA4DChEREZkcBhQiIiIyOQwoREREZHIYUIiIiMjkMKAQERGRyWFAISIiIpPDgEJEREQmx+RWM+5ejlutVktcCREREfVX9+d29+f4UJlcQGlqagIA+Pv7S1wJERERDVRTUxOcnZ2HfBxBNFbUMRK9Xo+Kigo4OjpCEIQBPVetVsPf3x+lpaVwcnIapgrNB89HTzwfPfF89MTz0RPPR088Hz31dT5EUURTUxN8fX0hkw29B4nJtaDIZDL4+fkN6RhOTk78AboNz0dPPB898Xz0xPPRE89HTzwfPd15PozRctKNnWSJiIjI5DCgEBERkcmxqICiVCrx+uuvQ6lUSl2KSeD56Innoyeej554Pnri+eiJ56OnkTgfJtdJloiIiMiiWlCIiIjIMjCgEBERkclhQCEiIiKTw4BCREREJsdiAkp2djYeeOABuLi4QKVS4Yc//CGam5sNj9fW1mLJkiXw9fWFUqmEv78/fvKTn1jsmj/3Ox9nz57F448/Dn9/f9ja2iI0NBR//vOfJax4eN3vfADAv/zLvyA2NhZKpRJRUVHSFDpC+nM+SkpKkJSUBDs7O3h6euJf//VfodVqJap4eF2+fBkrV66Eu7s7nJycMGvWLHz33Xc99jly5AhmzJgBR0dHeHt747XXXhvV5yMjIwOJiYlwcXGBq6srFi9ejLNnz0pU8fC63/n48MMPIQhCn7fq6moJKx8e/fn5ALrOS0REBGxsbODp6YkXX3xxQK9jEQGloqICCxcuxIQJE5CWloaDBw/i/Pnz+MEPfmDYRyaTYeXKldi9ezcuX76MDz/8EN9++y1+9KMfSVf4MOnP+cjKyoKnpye2bt2K8+fP45e//CU2bdqEd999V7rCh0l/zke3Z555BmvXrh35IkdQf86HTqdDUlISOjo6cPr0aXz00Uf48MMP8atf/Uq6wofRgw8+CK1Wi6NHjyIrKwuRkZF48MEHUVVVBaAr0C9btgxLlixBTk4OvvjiC+zevRu/+MUvJK58eNzvfDQ3N2PJkiUICAhAWloaTp48CUdHRyxevBidnZ0SV2989zsfa9euRWVlZY/b4sWLMXfuXHh6ekpcvfHd73wAwB//+Ef88pe/xC9+8QucP38e3377LRYvXjywFxItwHvvvSd6enqKOp3OsO3cuXMiAPHKlSt3fd6f//xn0c/PbyRKHFGDPR8//vGPxfnz549EiSNqoOfj9ddfFyMjI0ewwpHVn/Oxf/9+USaTiVVVVYZ9/va3v4lOTk6iRqMZ8ZqHU01NjQhAPHHihGGbWq0WAYjJycmiKIripk2bxLi4uB7P2717t2hjYyOq1eoRrXe49ed8ZGRkiADEkpISwz79+R1jjvpzPu5UXV0tWllZiR9//PFIlTli+nM+6urqRFtbW/Hbb78d0mtZRAuKRqOBtbV1j8WJbG1tAQAnT57s8zkVFRXYuXMn5s6dOyI1jqTBnA8AaGxshJub27DXN9IGez4sVX/Ox5kzZzBlyhR4eXkZ9lm8eDHUajXOnz8/sgUPM5VKhUmTJuHjjz9GS0sLtFot3nvvPXh6eiI2NhZA1zmzsbHp8TxbW1u0t7cjKytLirKHTX/Ox6RJk6BSqbBlyxZ0dHSgra0NW7ZsQWhoKAIDA6V9A0bWn/Nxp48//hh2dnZ4+OGHR7ja4def85GcnAy9Xo/y8nKEhobCz88Pjz76KEpLSwf2YkOKNyYiPz9fVCgU4n//93+LGo1GrKurE9esWSMCEH/3u9/12Pexxx4TbW1tRQDi8uXLxba2NomqHj4DOR/dTp06JSoUCvHQoUMjXO3wG+j5sPQWlP6cj+eee05ctGhRj+e1tLSIAMT9+/dLUfawKi0tFWNjY0VBEES5XC76+PiI2dnZhscPHTokymQy8bPPPhO1Wq1YVlYmzp49WwQgfvbZZxJWPjzudz5EURTz8vLEoKAgUSaTiTKZTJw0aZJYXFwsUcXDqz/n43ahoaHiCy+8MIIVjqz7nY+33npLtLKyEidNmiQePHhQPHPmjJiYmChOmjRpQC2wJt2C8otf/OKuHY+6bxcvXsTkyZPx0Ucf4Q9/+APs7Ozg7e2NcePGwcvLq9eSz3/605+QnZ2Nb775BkVFRXj11VclencDNxznAwDy8/OxcuVKvP7661i0aJEE72xwhut8mCuej576ez5EUcSLL74IT09PpKSkID09HQ899BCWL1+OyspKAMCiRYvw+9//Hj/60Y+gVCoRHByMZcuWAYDZnDNjno+2tjZs3LgRM2fORGpqKk6dOoXw8HAkJSWhra1N4nfaP8Y8H7c7c+YMCgoKsHHjRgne1eAZ83zo9Xp0dnbiL3/5CxYvXoxp06bh888/x5UrV/rsTHs3Jj3VfU1NDWpra++5z/jx42FtbW24f+PGDdjb20MQBDg5OWHbtm145JFH+nzuyZMnMXv2bFRUVMDHx8eotQ+H4TgfFy5cwPz58/Hss8/it7/97bDVPhyG6+fj17/+Nb7++mvk5uYOR9nDxpjn41e/+hV2797d4xxcu3YN48ePR3Z2NqKjo4frbRhNf89HSkoKFi1ahPr6+h7Lxk+cOBEbN27s0RFWFEVUVlbC1dUVxcXFCAsLQ3p6OuLj44ftfRiLMc/Hli1b8G//9m+orKw0BLSOjg64urpiy5YteOyxx4b1vRjDcPx8AMDGjRuRnZ2NnJycYal7uBjzfPzzn//EM888g9LSUvj5+Rn28fLywptvvonnnnuuXzUpBvdWRoaHhwc8PDwG9Jzua+YffPABbGxs8MADD9x1X71eD6Dr+rI5MPb5OH/+PBYsWICnn37a7MIJMPw/H+bGmOdj+vTp+O1vf4vq6mrDKITk5GQ4OTkhLCzMuIUPk/6ej9bWVgC9W0JkMpnhd0Q3QRDg6+sLAPj888/h7++PmJgYI1U8vIx5PlpbWyGTySAIQo/HBUHodc5M1XD8fDQ3N+PLL7/EW2+9ZbxCR4gxz8fMmTMBAJcuXTIElLq6Oty8eRNjx47tf1FGvTAlob/+9a9iVlaWeOnSJfHdd98VbW1txT//+c+Gx/ft2yd+8MEHYl5ennjt2jVx7969YmhoqDhz5kwJqx4+9zsfeXl5ooeHh7h+/XqxsrLScKuurpaw6uFzv/MhiqJ45coVMScnR3z++efF4OBgMScnR8zJybG4USuieP/zodVqxfDwcHHRokVibm6uePDgQdHDw0PctGmThFUPj5qaGlGlUomrV68Wc3NzxUuXLon/7//9P9HKykrMzc017Pff//3f4rlz58T8/HzxN7/5jWhlZSXu2rVLusKHSX/OR0FBgahUKsUXXnhBvHDhgpifny+uX79edHZ2FisqKiR+B8bV358PURTFf/zjH6KNjY1YX18vTbEjoL/nY+XKleLkyZPFU6dOiXl5eeKDDz4ohoWFiR0dHf1+LYsJKE8++aTo5uYmWltbixEREb2Gdx09elScPn266OzsLNrY2IgTJ04UX3vtNYv9Qbrf+Xj99ddFAL1uY8eOlabgYXa/8yGKojh37tw+z8m1a9dGvuBh1p/zUVxcLC5dulS0tbUV3d3dxZ/97GdiZ2enBNUOv4yMDHHRokWim5ub6OjoKE6bNq1XZ+D58+cbfn9MnTrVIjsLd+vP+Th8+LA4c+ZM0dnZWXR1dRUXLFggnjlzRqKKh1d/zocoiuL06dPFJ554QoIKR1Z/zkdjY6P4zDPPiC4uLqKbm5u4atWqHsPS+8Ok+6AQERHR6GQe3c+JiIhoVGFAISIiIpPDgEJEREQmhwGFiIiITA4DChEREZkcBhQiIiIyOQwoREREZHIYUIiIiMjkMKAQERGRyWFAISIiIpPDgEJEREQmhwGFiIiITM7/B+ZaNVEUNgBQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 59 HDs with major contig fails\n"
     ]
    }
   ],
   "source": [
    "contigHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in sPgenerator :\n",
    "    uCenterDist = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t]]\n",
    "    starterU = HDunitList[t][uCenterDist.index(np.min(uCenterDist))]\n",
    "    contigUs = getContigFromStarter(starterU,HDunitList[t],unitNbrs)\n",
    "    contigHDpop[t] = np.sum([unitPop[u] for u in contigUs] )\n",
    "print(\"here are the contig pops for discontig Us relative to aDP\")\n",
    "plt.hist([contigHDpop[t]/aDP for t in sPgenerator + doubleTroubleList])\n",
    "plt.show()\n",
    "print(\"... and here are the HD centers with contig pop < 0.7 * aDP ...\")\n",
    "FAILlist = list()\n",
    "for t in popHDlist:\n",
    "    if contigHDpop[t] < 0.7 * aDP:\n",
    "        FAILlist.append(t)\n",
    "        plotPoly(tractGeom[t])\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "print(\"there are\",len(FAILlist),\"HDs with major contig fails\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "90a50c12-7c3f-4c08-9b26-452cc5879a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing a couple of these major fails\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing a couple of these major fails\")\n",
    "n=30\n",
    "for t in FAILlist[n:n+2] :\n",
    "    #plotPoly(MAP)\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "    uCenterDist = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t]]\n",
    "    starterU = HDunitList[t][uCenterDist.index(np.min(uCenterDist))]\n",
    "    plotPoly(unitCP[starterU].buffer(0.05))\n",
    "    contigUs = getContigFromStarter(starterU,HDunitList[t],unitNbrs)\n",
    "    for u in HDunitList[t]:\n",
    "        if u in contigUs:\n",
    "            plotPoly(unitGeom[u])\n",
    "        else:\n",
    "            plotPoly(unitGeom[u],0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "05de6b91-17a8-4515-9a0e-edb2315fc0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linkSets = [set(), set()] #, set()]\n",
    "for u in borderSet:\n",
    "    if unitCP[u].x > -87.8 and unitCP[u].y < 44.3 and u not in coreUset:\n",
    "        linkSets[0].add(u)\n",
    "    if unitCP[u].x < -90. and unitCP[u].y < 44.75 and unitCP[u].y > 43 and u not in coreUset:\n",
    "        linkSets[1].add(u)\n",
    "    #if unitCP[u].x < -90 and unitCP[u].y < 43.5 and unitCP[u].y > 38.9 and u not in coreUset:\n",
    "    #    linkSets[2].add(u)\n",
    "for i,L in enumerate(linkSets):\n",
    "    for u in L:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(i,unitCP[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "d7716de0-4070-429b-952b-d3bf73476b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trying to border-Link 59 badly discontig HDs\n",
      "488 now contig but still has enclave\n",
      "489 now contig but still has enclave\n",
      "1334 now contig but still has enclave\n",
      "1339 now contig but still has enclave\n",
      "1955 now contig but still has enclave\n",
      "2340 now contig but still has enclave\n",
      "2467 now contig but still has enclave\n",
      "2498 now contig but still has enclave\n",
      "6033 now contig but still has enclave\n",
      "6125 now contig but still has enclave\n",
      "6153 now contig but still has enclave\n",
      "6172 now contig but still has enclave\n",
      "6174 now contig but still has enclave\n",
      "6175 now contig but still has enclave\n",
      "7016 now contig but still has enclave\n",
      "total number of fixedHDs = 27\n"
     ]
    }
   ],
   "source": [
    "hopeToFixHDs = FAILlist.copy()\n",
    "fixedHDs = list()\n",
    "print(\"trying to border-Link\",len(hopeToFixHDs),\"badly discontig HDs\")\n",
    "for t in hopeToFixHDs:\n",
    "    for L in linkSets:\n",
    "        testList = list(set(HDunitList[t]).union(L) ) \n",
    "        unbroken, noEnclave, __, ___ = enclaveCheck(testList, unitNbrs)   \n",
    "        canLink = unbroken\n",
    "        if unbroken and not noEnclave:\n",
    "            print(t,\"now contig but still has enclave\")  #oops, as run 7/7/24, printed ...\"and noEnclave\"\n",
    "        if canLink:\n",
    "            addedList = set(testList).difference( set(HDunitList[t]) )\n",
    "            fixedHDs.append(t)\n",
    "            HDunitList[t] += addedList\n",
    "            break\n",
    "print(\"total number of fixedHDs =\",len(fixedHDs))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "0686883a-3d02-4bee-8163-b788f5018159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rerunning CANFILL for the newly fixed HDs\n",
      "Now, let's fill in all enclaves that won't put us over 773550 district pop vs 736714 target\n",
      "working on problematic HD 6149 . We have evaluated 0 of 533 sketchy HDs.Time is now 0\n",
      "working on problematic HD 4139 . We have evaluated 20 of 533 sketchy HDs.Time is now 2\n",
      "working on problematic HD 145 . We have evaluated 40 of 533 sketchy HDs.Time is now 6\n",
      "working on problematic HD 2301 . We have evaluated 60 of 533 sketchy HDs.Time is now 10\n",
      "working on problematic HD 2346 . We have evaluated 80 of 533 sketchy HDs.Time is now 13\n",
      "working on problematic HD 350 . We have evaluated 100 of 533 sketchy HDs.Time is now 16\n",
      "working on problematic HD 432 . We have evaluated 120 of 533 sketchy HDs.Time is now 21\n",
      "working on problematic HD 467 . We have evaluated 140 of 533 sketchy HDs.Time is now 26\n",
      "working on problematic HD 2564 . We have evaluated 160 of 533 sketchy HDs.Time is now 30\n",
      "working on problematic HD 2584 . We have evaluated 180 of 533 sketchy HDs.Time is now 31\n",
      "working on problematic HD 2606 . We have evaluated 200 of 533 sketchy HDs.Time is now 33\n",
      "working on problematic HD 4668 . We have evaluated 220 of 533 sketchy HDs.Time is now 35\n",
      "working on problematic HD 6815 . We have evaluated 240 of 533 sketchy HDs.Time is now 39\n",
      "working on problematic HD 6844 . We have evaluated 260 of 533 sketchy HDs.Time is now 41\n",
      "working on problematic HD 2849 . We have evaluated 280 of 533 sketchy HDs.Time is now 44\n",
      "working on problematic HD 4993 . We have evaluated 300 of 533 sketchy HDs.Time is now 49\n",
      "working on problematic HD 932 . We have evaluated 320 of 533 sketchy HDs.Time is now 52\n",
      "working on problematic HD 3010 . We have evaluated 340 of 533 sketchy HDs.Time is now 56\n",
      "working on problematic HD 1086 . We have evaluated 360 of 533 sketchy HDs.Time is now 60\n",
      "working on problematic HD 1255 . We have evaluated 380 of 533 sketchy HDs.Time is now 65\n",
      "working on problematic HD 1334 . We have evaluated 400 of 533 sketchy HDs.Time is now 68\n",
      "working on problematic HD 1453 . We have evaluated 420 of 533 sketchy HDs.Time is now 71\n",
      "working on problematic HD 5635 . We have evaluated 440 of 533 sketchy HDs.Time is now 73\n",
      "working on problematic HD 5789 . We have evaluated 460 of 533 sketchy HDs.Time is now 78\n",
      "working on problematic HD 3824 . We have evaluated 480 of 533 sketchy HDs.Time is now 81\n",
      "working on problematic HD 3894 . We have evaluated 500 of 533 sketchy HDs.Time is now 86\n",
      "working on problematic HD 1971 . We have evaluated 520 of 533 sketchy HDs.Time is now 89\n",
      "Out of 533 of sketchy 336 were enclave-only.\n",
      "Of these, 7 wouldn't be over 773550 if all enclaves filled, while 329 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"rerunning CANFILL for the newly fixed HDs\")\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "thisList = set(eLgenerator + sPgenerator + doubleTroubleList)\n",
    "for iii, t in enumerate(thisList):\n",
    "    if iii%20 == 0:\n",
    "        print(\"working on problematic HD\",t,\". We have evaluated\",iii,\"of\",len(thisList),\"sketchy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(thisList),\"of sketchy\",len(tryToFill),\"were enclave-only.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "3c2d2e3b-0e6e-4698-b5cd-90a1b9bff214",
   "metadata": {},
   "outputs": [],
   "source": [
    "readyToMustFreeList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "a1128e1d-7b52-45d7-b8ae-b7055c28ff2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the MUSTFREE CODE\n",
      "this code will solve large enclaves so HD and complement are contiguous for 329 HDs\n",
      "working on HD 2055 cantFill 0 of 329 .Time is now 0\n",
      "working on HD 4229 cantFill 20 of 329 .Time is now 1\n",
      "working on HD 4389 cantFill 40 of 329 .Time is now 3\n",
      "working on HD 333 cantFill 60 of 329 .Time is now 5\n",
      "working on HD 465 cantFill 80 of 329 .Time is now 7\n",
      "working on HD 2569 cantFill 100 of 329 .Time is now 7\n",
      "working on HD 2591 cantFill 120 of 329 .Time is now 8\n",
      "working on HD 2613 cantFill 140 of 329 .Time is now 9\n",
      "working on HD 4687 cantFill 160 of 329 .Time is now 9\n",
      "working on HD 6832 cantFill 180 of 329 .Time is now 10\n",
      "working on HD 6953 cantFill 200 of 329 .Time is now 11\n",
      "working on HD 930 cantFill 220 of 329 .Time is now 13\n",
      "working on HD 1049 cantFill 240 of 329 .Time is now 14\n",
      "working on HD 1237 cantFill 260 of 329 .Time is now 16\n",
      "working on HD 5625 cantFill 280 of 329 .Time is now 19\n",
      "working on HD 3841 cantFill 300 of 329 .Time is now 20\n",
      "working on HD 3985 cantFill 320 of 329 .Time is now 20\n"
     ]
    }
   ],
   "source": [
    "print(\"run the MUSTFREE CODE\")\n",
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "targetList = cantFill.copy()\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(targetList),\"HDs\")\n",
    "for iii,t in enumerate(targetList):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(targetList),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "dad66700-9f5e-494f-8a99-7a91e0ee84d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.02637 0.12622\n",
      "CCB cluster 0 = unit 2970 with pop 13737.0 now has use of 1.0148839832513192\n",
      "CCB cluster 1 = unit 2971 with pop 169151.0 now has use of 1.0090852667195813\n",
      "CCB cluster 2 = unit 2972 with pop 171003.0 now has use of 1.0349745271151418\n",
      "CCB cluster 3 = unit 2973 with pop 92501.0 now has use of 1.021328811456537\n",
      "CCB cluster 4 = unit 2974 with pop 51938.0 now has use of 1.0200433749969038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "unitUse = [0.]*nUnits\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "9e18db82-86c6-41a4-84de-7f617f4d5d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 10\n",
      "working on HD 1400 time is now 20\n",
      "working on HD 2100 time is now 29\n",
      "working on HD 2800 time is now 36\n",
      "working on HD 3500 time is now 44\n",
      "working on HD 4202 time is now 51\n",
      "working on HD 4902 time is now 60\n",
      "working on HD 5602 time is now 70\n",
      "working on HD 6302 time is now 83\n",
      "working on HD 7002 time is now 95\n",
      "out of 7057 total HDs, there were 6802.0 contiguous and 6967.0 complement-contiguous HDs\n",
      "90 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 165\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"updated classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "ad729d71-248b-4b35-96d9-aad02fc7285e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have a total of 255 stubborn HDs with contiguity problems\n",
      "All HDdiam's calculated\n"
     ]
    }
   ],
   "source": [
    "preMustSwellList = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "triageList = list(set(sPgenerator+eLgenerator+doubleTroubleList))\n",
    "listToFix = list()\n",
    "for t in triageList:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)\n",
    "    if not (unbroken and noEnclave):\n",
    "        listToFix.append(t)\n",
    "\n",
    "print(\"we have a total of\",len(listToFix),\"stubborn HDs with contiguity problems\")\n",
    "HDdiam = [0.]*nHDs  #about 1 min per 4000 500-unit HDs\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:  #create a crude average diameter = weighted mean distance from each unit to center\n",
    "        HDdiam[t] += unitPop[u]/HDvPop[t] * unitCP[u].distance(hdCP[t])\n",
    "print(\"All HDdiam's calculated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "1e8405de-1ca0-4309-9de9-b7d7599246d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now force all in 255 listToFix to be contiguous and nil-enclave\n",
      "Let's first recalc unitUse\n",
      "Strategy:  1 - reduce HD to its primary contiguous piece.\n",
      "  If this is enclavy, fill enclaves if can do under aDP.  If too much enclave pop, remove some border units from this contig piece\n",
      "  If discontig piece not enclavy, simply grow contiguously until fill\n",
      "fixing HD 513 0 of 255 elapsed sec = 0\n",
      "fixing HD 6671 10 of 255 elapsed sec = 6\n",
      "fixing HD 30 20 of 255 elapsed sec = 29\n",
      "fixing HD 2615 30 of 255 elapsed sec = 35\n",
      "fixing HD 4687 40 of 255 elapsed sec = 39\n",
      "fixing HD 125 50 of 255 elapsed sec = 62\n",
      "fixing HD 5779 60 of 255 elapsed sec = 106\n",
      "fixing HD 2719 70 of 255 elapsed sec = 148\n",
      "fixing HD 1705 80 of 255 elapsed sec = 199\n",
      "fixing HD 6348 90 of 255 elapsed sec = 206\n",
      "fixing HD 1255 100 of 255 elapsed sec = 225\n",
      "fixing HD 2815 110 of 255 elapsed sec = 282\n",
      "fixing HD 3855 120 of 255 elapsed sec = 357\n",
      "fixing HD 2854 130 of 255 elapsed sec = 923\n",
      "fixing HD 3381 140 of 255 elapsed sec = 941\n",
      "fixing HD 3418 150 of 255 elapsed sec = 977\n",
      "fixing HD 1395 160 of 255 elapsed sec = 1166\n",
      "fixing HD 902 170 of 255 elapsed sec = 1256\n",
      "fixing HD 919 180 of 255 elapsed sec = 1336\n",
      "fixing HD 932 190 of 255 elapsed sec = 1470\n",
      "fixing HD 1455 200 of 255 elapsed sec = 1583\n",
      "fixing HD 952 210 of 255 elapsed sec = 1599\n",
      "fixing HD 3010 220 of 255 elapsed sec = 1680\n",
      "fixing HD 4560 230 of 255 elapsed sec = 1804\n",
      "fixing HD 5092 240 of 255 elapsed sec = 1884\n",
      "fixing HD 1013 250 of 255 elapsed sec = 1901\n"
     ]
    }
   ],
   "source": [
    "print(\"we will now force all in\",len(listToFix),\"listToFix to be contiguous and nil-enclave\")\n",
    "print(\"Let's first recalc unitUse\")\n",
    "unitUse = [0.]*nUnits\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "print(\"Strategy:  1 - reduce HD to its primary contiguous piece.\")\n",
    "print(\"  If this is enclavy, fill enclaves if can do under aDP.  If too much enclave pop, remove some border units from this contig piece\")\n",
    "print(\"  If discontig piece not enclavy, simply grow contiguously until fill\")\n",
    "CCBset = set()\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "for j in range(nCCBs):\n",
    "    CCBset.add( allUnits.index(j+0.25) )\n",
    "    \n",
    "for ijk,t in enumerate(listToFix):\n",
    "    if ijk%10 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"fixing HD\",t,ijk,\"of\",len(listToFix),\"elapsed sec =\",int(SEC) )\n",
    "    uCenterDist = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t]]\n",
    "    starterU = HDunitList[t][uCenterDist.index(np.min(uCenterDist))]\n",
    "    contigUs = getContigFromStarter(starterU,HDunitList[t],unitNbrs)\n",
    "    contigPop = np.sum([unitPop[u] for u in contigUs])\n",
    "    enclaveLists = getEnclaveLists(contigUs,unitNbrs)\n",
    "    ePop = np.sum([np.sum([unitPop[uu] for uu in eL]) for eL in enclaveLists])\n",
    "    shedBorderUset = set()\n",
    "    if ePop < 0.1 * aDP or contigPop + ePop < 1.2 * aDP:\n",
    "        contigList = contigUs.copy()\n",
    "        for eL in enclaveLists:\n",
    "            contigList += eL\n",
    "    else:  #severe enclave.  Jettison all but closest contiguous set of in HD border units, unless ...\n",
    "        #   we have a CCBu, then that is the starter for the contig boundary list we're saving (to not jettison too many CCBus)\n",
    "        contigBorderUs = list(set(contigUs).intersection(borderSet))\n",
    "        if len(contigBorderUs) > 0:\n",
    "            hasCCBu, CCBu = False, -999\n",
    "            CCBsInHD = set(contigBorderUs).intersection(CCBset)\n",
    "            for u in CCBsInHD:\n",
    "                hasCCBu = True  #these will be found in random order\n",
    "                CCBu = u\n",
    "            if hasCCBu:\n",
    "                starterBorderU = CCBu\n",
    "            else:\n",
    "                borderDist = [hdCP[t].distance(unitCP[u]) for u in contigBorderUs]\n",
    "                starterBorderU = borderDist.index(np.min(borderDist))\n",
    "            keptBorderUs = getContigFromStarter(starterBorderU, contigBorderUs, unitNbrs)\n",
    "            shedBorderUset = set(contigBorderUs).difference(set(keptBorderUs))  #this is like a MUSTFREE operation\n",
    "    contigList = list( set(contigList).difference(shedBorderUset) )\n",
    "\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(contigList, unitNbrs,6)\n",
    "    if not (unbroken and noEnclave):  #likely that jettisoning border units created a discontiguity\n",
    "        contigList = [starterU]  #KISS\n",
    "        print(\"for HD\",t,\"we are starting from the home unit\",starterU,\". Working on fixable HD\",ijk)\n",
    "    \n",
    "    contigPop = np.sum([unitPop[u] for u in contigList])\n",
    "    currPoly = hdCP[t]\n",
    "    for u in contigList:\n",
    "        currPoly = currPoly.union(unitCP[u])  #draw a crude 'polygon' = points collection for below distance calc\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigList.copy(), list()\n",
    "    adjoiners = list(set(getAdjoiners(currList, unitNbrs)).difference(coreUset))  #exclude core VRA units\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "        currPoly) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "    stillGoing = True\n",
    "\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in coreUset:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                        currPoly) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigList)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigList + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    #HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "89d08c99-0597-4f73-b5bf-23dbf57d8fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 9\n",
      "working on HD 1400 time is now 16\n",
      "working on HD 2100 time is now 23\n",
      "working on HD 2800 time is now 28\n",
      "working on HD 3500 time is now 35\n",
      "working on HD 4202 time is now 42\n",
      "working on HD 4902 time is now 48\n",
      "working on HD 5602 time is now 57\n",
      "working on HD 6302 time is now 64\n",
      "working on HD 7002 time is now 72\n",
      "out of 7057 total HDs, there were 7057.0 contiguous and 7057.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"updated classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "6c1fadc4-cf37-4a8d-9865-d01221f39a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"VRAcontigOffPop.csv\" #\"contigUnpatchedB.csv\"  #underPatched\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "be7851e4-8a10-4a64-9b4e-afdab9048078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 1806 HDs with pop < 733031 to within 1551\n",
      "working on squaring up HD 0 time is now 0\n",
      "working on squaring up HD 181 time is now 106\n",
      "working on squaring up HD 313 time is now 113\n",
      "working on squaring up HD 418 time is now 118\n",
      "working on squaring up HD 514 time is now 125\n",
      "working on squaring up HD 651 time is now 136\n",
      "working on squaring up HD 759 time is now 169\n",
      "working on squaring up HD 850 time is now 186\n",
      "working on squaring up HD 1054 time is now 215\n",
      "working on squaring up HD 1189 time is now 225\n",
      "working on squaring up HD 1364 time is now 289\n",
      "working on squaring up HD 1463 time is now 291\n",
      "working on squaring up HD 1616 time is now 296\n",
      "working on squaring up HD 1745 time is now 307\n",
      "working on squaring up HD 1974 time is now 317\n",
      "working on squaring up HD 2061 time is now 356\n",
      "working on squaring up HD 2231 time is now 380\n",
      "working on squaring up HD 2417 time is now 382\n",
      "working on squaring up HD 2643 time is now 408\n",
      "working on squaring up HD 2810 time is now 456\n",
      "working on squaring up HD 2926 time is now 473\n",
      "working on squaring up HD 3035 time is now 482\n",
      "working on squaring up HD 3230 time is now 492\n",
      "working on squaring up HD 3486 time is now 501\n",
      "working on squaring up HD 3728 time is now 514\n",
      "working on squaring up HD 3869 time is now 523\n",
      "working on squaring up HD 4035 time is now 527\n",
      "working on squaring up HD 4209 time is now 531\n",
      "working on squaring up HD 4372 time is now 534\n",
      "working on squaring up HD 4491 time is now 568\n",
      "working on squaring up HD 4653 time is now 577\n",
      "working on squaring up HD 4771 time is now 582\n",
      "working on squaring up HD 4865 time is now 591\n",
      "working on squaring up HD 4958 time is now 603\n",
      "working on squaring up HD 5055 time is now 605\n",
      "working on squaring up HD 5317 time is now 608\n",
      "working on squaring up HD 5616 time is now 611\n",
      "working on squaring up HD 5743 time is now 622\n",
      "working on squaring up HD 5918 time is now 632\n",
      "working on squaring up HD 6118 time is now 640\n",
      "working on squaring up HD 6209 time is now 644\n",
      "working on squaring up HD 6463 time is now 646\n",
      "working on squaring up HD 6621 time is now 655\n",
      "working on squaring up HD 6717 time is now 660\n",
      "working on squaring up HD 6837 time is now 680\n",
      "working on squaring up HD 7047 time is now 691\n",
      "We have addressed underpop in a total of 1806 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED - here, we've not yet adopted avoidedEnclave but we now disallow picking up VRA coreUs\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.995 * aDP, 1.005 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%40 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist+coreUs and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits) \n",
    "            #       not sure if avoidedEnclave(HDunitList[t], unitNbrs, [unitNoToAdd]) is robust\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist+coreUs and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "fe4dfd92-8bc5-4e76-b2a2-76f60b2e66dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amped unit avg and sd usage are 1.03126 0.12223\n"
     ]
    },
    {
     "data": {
      "image/png": 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5ubnGGBNQL4Hwer1GkvF6vQE/B0DzKz5QZrrPWGWKD5RdUA2A1imYz+/zniNUVVWlP/3pTxo/frxCQkJUWFio6upqpaWlWTW9e/dWt27dVFBQIEkqKChQ//79FR8fb9W43W75fD59+umnVs2p26irqdtGVVWVCgsL/WpCQ0OVlpZm1QTSy9lUVlbK5/P5LQAAoO067yD01ltvqaysTHfffbckyePxKCIiQrGxsX518fHx8ng8Vs2pIahuvG6svhqfz6eKigp9/fXXqqmpOWvNqdtoqJezyc3NVUxMjLUkJSU1/EIAAIBW67yD0Msvv6xbbrlFiYmJjdlPs5o5c6a8Xq+17N+/v7lbAgAAF1G783nSv/71L7377rt64403rHUJCQmqqqpSWVmZ35GYkpISJSQkWDWnn91VdybXqTWnn91VUlIip9OpqKgohYWFKSws7Kw1p26joV7OxuFwyOFwBPgqAACA1u68jgi98soriouLU3p6urVu8ODBCg8PV35+vrVu9+7d2rdvn1wulyTJ5XKpuLjY7+yudevWyel0qk+fPlbNqduoq6nbRkREhAYPHuxXU1tbq/z8fKsmkF4AAACCPiJUW1urV155RRkZGWrX7v+eHhMTowkTJig7O1udOnWS0+nU/fffL5fLpWuvvVaSdNNNN6lPnz666667NHfuXHk8Hs2aNUuZmZnWkZhJkybp+eef1/Tp0zV+/HitX79eK1asUF5enrWv7OxsZWRkaMiQIUpJSdG8efNUXl6ucePGBdwLAABA0EHo3Xff1b59+zR+/Pgzxp555hmFhoZq1KhRqqyslNvt1sKFC63xsLAwrVq1SpMnT5bL5VKHDh2UkZGhRx991KpJTk5WXl6epk6dqvnz56tr16566aWX5Ha7rZrRo0fr8OHDysnJkcfj0cCBA7VmzRq/CdQN9QIAABBijDHN3URL5fP5FBMTI6/XK6fT2dztAAjQjoNe3frce1p1/1D16xJz3jUAWqdgPr+51xgAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALCtoIPQwYMH9dOf/lSdO3dWVFSU+vfvr48++sgaN8YoJydHl19+uaKiopSWlqbPP//cbxtHjhzR2LFj5XQ6FRsbqwkTJuj48eN+NZ988omuu+46RUZGKikpSXPnzj2jl5UrV6p3796KjIxU//79tXr1ar/xQHoBAAD2FVQQOnr0qH7wgx8oPDxc77zzjnbu3KmnnnpKHTt2tGrmzp2rZ599VosXL9aWLVvUoUMHud1unThxwqoZO3asPv30U61bt06rVq3Spk2bdO+991rjPp9PN910k7p3767CwkI9+eSTeuSRR/TCCy9YNZs3b9aYMWM0YcIEbd++XSNHjtTIkSO1Y8eOoHoBAAA2ZoIwY8YMM3To0HOO19bWmoSEBPPkk09a68rKyozD4TBLly41xhizc+dOI8l8+OGHVs0777xjQkJCzMGDB40xxixcuNB07NjRVFZW+u27V69e1uPbb7/dpKen++0/NTXV3HfffQH30hCv12skGa/XG1A9gJah+ECZ6T5jlSk+UHZBNQBap2A+v4M6IvSXv/xFQ4YM0U9+8hPFxcXpmmuu0YsvvmiN7927Vx6PR2lpada6mJgYpaamqqCgQJJUUFCg2NhYDRkyxKpJS0tTaGiotmzZYtVcf/31ioiIsGrcbrd2796to0ePWjWn7qeupm4/gfRyusrKSvl8Pr8FAAC0XUEFoS+++EKLFi1Sz549tXbtWk2ePFm/+MUv9Oqrr0qSPB6PJCk+Pt7vefHx8daYx+NRXFyc33i7du3UqVMnv5qzbePUfZyr5tTxhno5XW5urmJiYqwlKSmpoZcEAAC0YkEFodraWg0aNEi//e1vdc011+jee+/VPffco8WLF1+s/prUzJkz5fV6rWX//v3N3RIAALiIggpCl19+ufr06eO37qqrrtK+ffskSQkJCZKkkpISv5qSkhJrLCEhQaWlpX7jJ0+e1JEjR/xqzraNU/dxrppTxxvq5XQOh0NOp9NvAQAAbVdQQegHP/iBdu/e7bfus88+U/fu3SVJycnJSkhIUH5+vjXu8/m0ZcsWuVwuSZLL5VJZWZkKCwutmvXr16u2tlapqalWzaZNm1RdXW3VrFu3Tr169bLOUHO5XH77qaup208gvQAAAJsLZhb21q1bTbt27czjjz9uPv/8c/Paa6+Z9u3bmz/96U9WzRNPPGFiY2PNn//8Z/PJJ5+YH//4xyY5OdlUVFRYNTfffLO55pprzJYtW8x7771nevbsacaMGWONl5WVmfj4eHPXXXeZHTt2mGXLlpn27dub3//+91bN+++/b9q1a2d+97vfmV27dpnZs2eb8PBwU1xcHFQv9eGsMaB14qwxwN6C+fwOKggZY8zbb79t+vXrZxwOh+ndu7d54YUX/MZra2vNww8/bOLj443D4TA33nij2b17t1/NN998Y8aMGWOio6ON0+k048aNM8eOHfOr+fjjj83QoUONw+EwXbp0MU888cQZvaxYscJceeWVJiIiwvTt29fk5eUF3Ut9CEJA60QQAuwtmM/vEGOMad5jUi2Xz+dTTEyMvF4v84WAVmTHQa9ufe49rbp/qPp1iTnvGgCtUzCf39xrDAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2FZQQeiRRx5RSEiI39K7d29r/MSJE8rMzFTnzp0VHR2tUaNGqaSkxG8b+/btU3p6utq3b6+4uDhNmzZNJ0+e9KvZsGGDBg0aJIfDoR49emjJkiVn9LJgwQJdccUVioyMVGpqqrZu3eo3HkgvAADA3oI+ItS3b1999dVX1vLee+9ZY1OnTtXbb7+tlStXauPGjTp06JBuu+02a7ympkbp6emqqqrS5s2b9eqrr2rJkiXKycmxavbu3av09HQNHz5cRUVFysrK0sSJE7V27VqrZvny5crOztbs2bO1bds2DRgwQG63W6WlpQH3AgAAIBOE2bNnmwEDBpx1rKyszISHh5uVK1da63bt2mUkmYKCAmOMMatXrzahoaHG4/FYNYsWLTJOp9NUVlYaY4yZPn266du3r9+2R48ebdxut/U4JSXFZGZmWo9rampMYmKiyc3NDbiXQHi9XiPJeL3egJ8DoPkVHygz3WesMsUHyi6oBkDrFMznd9BHhD7//HMlJibqO9/5jsaOHat9+/ZJkgoLC1VdXa20tDSrtnfv3urWrZsKCgokSQUFBerfv7/i4+OtGrfbLZ/Pp08//dSqOXUbdTV126iqqlJhYaFfTWhoqNLS0qyaQHo5m8rKSvl8Pr8FAAC0XUEFodTUVC1ZskRr1qzRokWLtHfvXl133XU6duyYPB6PIiIiFBsb6/ec+Ph4eTweSZLH4/ELQXXjdWP11fh8PlVUVOjrr79WTU3NWWtO3UZDvZxNbm6uYmJirCUpKSmwFwYAALRK7YIpvuWWW6z/vvrqq5Wamqru3btrxYoVioqKavTmmtrMmTOVnZ1tPfb5fIQhAADasAs6fT42NlZXXnml9uzZo4SEBFVVVamsrMyvpqSkRAkJCZKkhISEM87cqnvcUI3T6VRUVJQuvfRShYWFnbXm1G001MvZOBwOOZ1OvwUAALRdFxSEjh8/rn/+85+6/PLLNXjwYIWHhys/P98a3717t/bt2yeXyyVJcrlcKi4u9ju7a926dXI6nerTp49Vc+o26mrqthEREaHBgwf71dTW1io/P9+qCaQXAACAoL4a++Uvf6kRI0aoe/fuOnTokGbPnq2wsDCNGTNGMTExmjBhgrKzs9WpUyc5nU7df//9crlcuvbaayVJN910k/r06aO77rpLc+fOlcfj0axZs5SZmSmHwyFJmjRpkp5//nlNnz5d48eP1/r167VixQrl5eVZfWRnZysjI0NDhgxRSkqK5s2bp/Lyco0bN06SAuoFAAAgqCB04MABjRkzRt98840uu+wyDR06VB988IEuu+wySdIzzzyj0NBQjRo1SpWVlXK73Vq4cKH1/LCwMK1atUqTJ0+Wy+VShw4dlJGRoUcffdSqSU5OVl5enqZOnar58+era9eueumll+R2u62a0aNH6/Dhw8rJyZHH49HAgQO1Zs0avwnUDfUCAAAQYowxzd1ES+Xz+RQTEyOv18t8IaAV2XHQq1ufe0+r7h+qfl1izrsGQOsUzOc39xoDAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC21a65GwDQ8h0sq9DR8qp6azp2iFCX2Kgm6ggAGgdBCGilmiqcHCyrUNpTG1VRXVNvXVR4mN59cBhhCECrQhACWqGmDCdHy6tUUV2jeaMHqkdc9Flr9pQeV9byIh0tryIIAWhVCEJAK9Qc4aRHXLT6dYm54O0AQEtCEAJaMcIJAFwYzhoDAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2RRACAAC2dUFB6IknnlBISIiysrKsdSdOnFBmZqY6d+6s6OhojRo1SiUlJX7P27dvn9LT09W+fXvFxcVp2rRpOnnypF/Nhg0bNGjQIDkcDvXo0UNLliw5Y/8LFizQFVdcocjISKWmpmrr1q1+44H0AgAA7Ou8g9CHH36o3//+97r66qv91k+dOlVvv/22Vq5cqY0bN+rQoUO67bbbrPGamhqlp6erqqpKmzdv1quvvqolS5YoJyfHqtm7d6/S09M1fPhwFRUVKSsrSxMnTtTatWutmuXLlys7O1uzZ8/Wtm3bNGDAALndbpWWlgbcCwAAsLfzCkLHjx/X2LFj9eKLL6pjx47Weq/Xq5dffllPP/20fvjDH2rw4MF65ZVXtHnzZn3wwQeSpL/+9a/auXOn/vSnP2ngwIG65ZZb9Nhjj2nBggWqqvr3fZMWL16s5ORkPfXUU7rqqqs0ZcoU/fd//7eeeeYZa19PP/207rnnHo0bN059+vTR4sWL1b59e/3hD38IuBcAAGBv5xWEMjMzlZ6errS0NL/1hYWFqq6u9lvfu3dvdevWTQUFBZKkgoIC9e/fX/Hx8VaN2+2Wz+fTp59+atWcvm23221to6qqSoWFhX41oaGhSktLs2oC6eV0lZWV8vl8fgsAAGi7gr7FxrJly7Rt2zZ9+OGHZ4x5PB5FREQoNjbWb318fLw8Ho9Vc2oIqhuvG6uvxufzqaKiQkePHlVNTc1Za/7xj38E3MvpcnNzNWfOnHp+egAA0JYEdURo//79euCBB/Taa68pMjLyYvXUbGbOnCmv12st+/fvb+6WAADARRRUECosLFRpaakGDRqkdu3aqV27dtq4caOeffZZtWvXTvHx8aqqqlJZWZnf80pKSpSQkCBJSkhIOOPMrbrHDdU4nU5FRUXp0ksvVVhY2FlrTt1GQ72czuFwyOl0+i0AAKDtCioI3XjjjSouLlZRUZG1DBkyRGPHjrX+Ozw8XPn5+dZzdu/erX379snlckmSXC6XiouL/c7uWrdunZxOp/r06WPVnLqNupq6bURERGjw4MF+NbW1tcrPz7dqBg8e3GAvAADA3oKaI3TJJZeoX79+fus6dOigzp07W+snTJig7OxsderUSU6nU/fff79cLpeuvfZaSdJNN92kPn366K677tLcuXPl8Xg0a9YsZWZmyuFwSJImTZqk559/XtOnT9f48eO1fv16rVixQnl5edZ+s7OzlZGRoSFDhiglJUXz5s1TeXm5xo0bJ0mKiYlpsBcAAGBvQU+Wbsgzzzyj0NBQjRo1SpWVlXK73Vq4cKE1HhYWplWrVmny5MlyuVzq0KGDMjIy9Oijj1o1ycnJysvL09SpUzV//nx17dpVL730ktxut1UzevRoHT58WDk5OfJ4PBo4cKDWrFnjN4G6oV4AAIC9XXAQ2rBhg9/jyMhILViwQAsWLDjnc7p3767Vq1fXu90bbrhB27dvr7dmypQpmjJlyjnHA+kFAADYF/caAwAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAttXop88DaF0OllXoaHnVOcf3lB5vwm4AoGkRhAAbO1hWobSnNqqiuqbeuqjwMHXsENFEXQFA0yEIATZ2tLxKFdU1mjd6oHrERZ+zrmOHCHWJjWrCzgCgaRCEAKhHXLT6dYlp7jYAoMkxWRoAANgWQQgAANgWQQgAANgWc4QANJqGTrVn0jWAloYgBOCCdewQoajwMGUtL6q3Lio8TO8+OIwwBKDFIAgBuGBdYqP07oPDGrwwY9byIh0tryIIAWgxCEIAGkWX2CgCDoBWh8nSAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtghCAADAtoIKQosWLdLVV18tp9Mpp9Mpl8uld955xxo/ceKEMjMz1blzZ0VHR2vUqFEqKSnx28a+ffuUnp6u9u3bKy4uTtOmTdPJkyf9ajZs2KBBgwbJ4XCoR48eWrJkyRm9LFiwQFdccYUiIyOVmpqqrVu3+o0H0gsAALC3oIJQ165d9cQTT6iwsFAfffSRfvjDH+rHP/6xPv30U0nS1KlT9fbbb2vlypXauHGjDh06pNtuu816fk1NjdLT01VVVaXNmzfr1Vdf1ZIlS5STk2PV7N27V+np6Ro+fLiKioqUlZWliRMnau3atVbN8uXLlZ2drdmzZ2vbtm0aMGCA3G63SktLrZqGegEAAJC5QB07djQvvfSSKSsrM+Hh4WblypXW2K5du4wkU1BQYIwxZvXq1SY0NNR4PB6rZtGiRcbpdJrKykpjjDHTp083ffv29dvH6NGjjdvtth6npKSYzMxM63FNTY1JTEw0ubm5xhgTUC+B8Hq9RpLxer0BPwdoCsUHykz3GatM8YGyi17TWFravpqyHwBNK5jP7/OeI1RTU6Nly5apvLxcLpdLhYWFqq6uVlpamlXTu3dvdevWTQUFBZKkgoIC9e/fX/Hx8VaN2+2Wz+ezjioVFBT4baOupm4bVVVVKiws9KsJDQ1VWlqaVRNIL2dTWVkpn8/ntwAAgLYr6CBUXFys6OhoORwOTZo0SW+++ab69Okjj8ejiIgIxcbG+tXHx8fL4/FIkjwej18IqhuvG6uvxufzqaKiQl9//bVqamrOWnPqNhrq5Wxyc3MVExNjLUlJSYG9KAAAoFUKOgj16tVLRUVF2rJliyZPnqyMjAzt3LnzYvTW5GbOnCmv12st+/fvb+6WAADARdQu2CdERESoR48ekqTBgwfrww8/1Pz58zV69GhVVVWprKzM70hMSUmJEhISJEkJCQlnnN1VdybXqTWnn91VUlIip9OpqKgohYWFKSws7Kw1p26joV7OxuFwyOFwBPFqAACA1uyCryNUW1uryspKDR48WOHh4crPz7fGdu/erX379snlckmSXC6XiouL/c7uWrdunZxOp/r06WPVnLqNupq6bURERGjw4MF+NbW1tcrPz7dqAukFAAAgqCNCM2fO1C233KJu3brp2LFjev3117VhwwatXbtWMTExmjBhgrKzs9WpUyc5nU7df//9crlcuvbaayVJN910k/r06aO77rpLc+fOlcfj0axZs5SZmWkdiZk0aZKef/55TZ8+XePHj9f69eu1YsUK5eXlWX1kZ2crIyNDQ4YMUUpKiubNm6fy8nKNGzdOkgLqBQAAIKggVFpaqp/97Gf66quvFBMTo6uvvlpr167Vj370I0nSM888o9DQUI0aNUqVlZVyu91auHCh9fywsDCtWrVKkydPlsvlUocOHZSRkaFHH33UqklOTlZeXp6mTp2q+fPnq2vXrnrppZfkdrutmtGjR+vw4cPKycmRx+PRwIEDtWbNGr8J1A31AgAAEFQQevnll+sdj4yM1IIFC7RgwYJz1nTv3l2rV6+udzs33HCDtm/fXm/NlClTNGXKlAvqBQAA2Bv3GgMAALYV9FljAC7MwbIKHS2vqremY4cIdYmNaqKOAMC+CEJAEzpYVqG0pzaqorqm3rqo8DC9++AwwhAAXGQEIaAJHS2vUkV1jeaNHqgecdFnrdlTelxZy4t0tLyKIAQAFxlBCGgGPeKi1a9LTHO3AQC2x2RpAABgWxwRAtq4PaXHz2sMAOyAIAS0UR07RCgqPExZy4vqrYsKD1PHDhFN0xQAtDAEIaCN6hIbpXcfHMap+gBQD4IQ0IZ1iY0i5ABAPZgsDQAAbIsgBAAAbIsgBAAAbIsgBAAAbIsgBAAAbIsgBAAAbIsgBAAAbIsgBAAAbIsLKgKN6GBZRb1XcubeXgDQshCEgEZysKxCaU9tVEV1Tb113NsLAFoOghDQSI6WV6miukbzRg9Uj7joc9Zxby8AaDkIQkAj6xEXrX5dYpq7DQBAAJgsDQAAbIsgBAAAbIuvxoAAcUYYALQ9BCEgAJwRBgBtE0EICABnhAFA20QQAoLAGWEA0LYwWRoAANgWQQgAANgWQQgAANgWQQgAANgWQQgAANhWUEEoNzdX3/ve93TJJZcoLi5OI0eO1O7du/1qTpw4oczMTHXu3FnR0dEaNWqUSkpK/Gr27dun9PR0tW/fXnFxcZo2bZpOnjzpV7NhwwYNGjRIDodDPXr00JIlS87oZ8GCBbriiisUGRmp1NRUbd26NeheAACAfQUVhDZu3KjMzEx98MEHWrdunaqrq3XTTTepvLzcqpk6darefvttrVy5Uhs3btShQ4d02223WeM1NTVKT09XVVWVNm/erFdffVVLlixRTk6OVbN3716lp6dr+PDhKioqUlZWliZOnKi1a9daNcuXL1d2drZmz56tbdu2acCAAXK73SotLQ24FwAAYHPmApSWlhpJZuPGjcYYY8rKykx4eLhZuXKlVbNr1y4jyRQUFBhjjFm9erUJDQ01Ho/Hqlm0aJFxOp2msrLSGGPM9OnTTd++ff32NXr0aON2u63HKSkpJjMz03pcU1NjEhMTTW5ubsC9NMTr9RpJxuv1BlSPtqv4QJnpPmOVKT5Q1iL21ZT9NBZeQwBNJZjP7wuaI+T1eiVJnTp1kiQVFhaqurpaaWlpVk3v3r3VrVs3FRQUSJIKCgrUv39/xcfHWzVut1s+n0+ffvqpVXPqNupq6rZRVVWlwsJCv5rQ0FClpaVZNYH0crrKykr5fD6/BQAAtF3nHYRqa2uVlZWlH/zgB+rXr58kyePxKCIiQrGxsX618fHx8ng8Vs2pIahuvG6svhqfz6eKigp9/fXXqqmpOWvNqdtoqJfT5ebmKiYmxlqSkpICfDUAAEBrdN5BKDMzUzt27NCyZcsas59mNXPmTHm9XmvZv39/c7cEAAAuovO619iUKVO0atUqbdq0SV27drXWJyQkqKqqSmVlZX5HYkpKSpSQkGDVnH52V92ZXKfWnH52V0lJiZxOp6KiohQWFqawsLCz1py6jYZ6OZ3D4ZDD4QjilQAAAK1ZUEeEjDGaMmWK3nzzTa1fv17Jycl+44MHD1Z4eLjy8/Otdbt379a+ffvkcrkkSS6XS8XFxX5nd61bt05Op1N9+vSxak7dRl1N3TYiIiI0ePBgv5ra2lrl5+dbNYH0ArRke0qPa8dB71mXPaXHm7u981bfz7XjoFcHyyqau0UANhLUEaHMzEy9/vrr+vOf/6xLLrnEmmsTExOjqKgoxcTEaMKECcrOzlanTp3kdDp1//33y+Vy6dprr5Uk3XTTTerTp4/uuusuzZ07Vx6PR7NmzVJmZqZ1NGbSpEl6/vnnNX36dI0fP17r16/XihUrlJeXZ/WSnZ2tjIwMDRkyRCkpKZo3b57Ky8s1btw4q6eGegFaoo4dIhQVHqas5UX11kWFh6ljh4imaaoRBPNzvfvgMHWJjWqaxgDYWlBBaNGiRZKkG264wW/9K6+8orvvvluS9Mwzzyg0NFSjRo1SZWWl3G63Fi5caNWGhYVp1apVmjx5slwulzp06KCMjAw9+uijVk1ycrLy8vI0depUzZ8/X127dtVLL70kt9tt1YwePVqHDx9WTk6OPB6PBg4cqDVr1vhNoG6oF6Al6hIbpXcfHKaj5VX11nXsENGqwkIgP9ee0uPKWl6ko+VVrepnA9B6BRWEjDEN1kRGRmrBggVasGDBOWu6d++u1atX17udG264Qdu3b6+3ZsqUKZoyZcoF9QK0RF1io9pkEGirPxeA1ot7jQEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANs6r3uNAcDF1NAtRFrbxSQBtFwEIQAtBrfhANDUCEIAWgxuwwGgqRGEALQo3IYDQFNisjQAALAtjggBwAU6WFZR79d5EhO8gZaKIAQAF+BgWYXSntqoiuqaeuuY4A20TAQhALgAR8urVFFdo3mjB6pHXPRZa5jgDbRcBCEAaAQ94qLVr0tMc7cBIEhMlgYAALZFEAIAALZFEAIAALZFEAIAALbFZGlADV8HpqGbgAIAWieCEGwvmOvAdOwQ0URdAQCaAkEIthfIdWAkrgwMAG0RQQj4/3EdGACwHyZLAwAA2yIIAQAA2+KrMQBoIo1x9iFz1YDGRRACgIusY4cIRYWHKWt50QVvi7vYA42LIAQAF1mX2Ci9++Cweq9VFQjuYg80PoIQgFapvq+ZGvMCmI11sc0usVGEF6AFIgihzeOq0W1LoF8zNcYFMLnYJtD2EYTQpvFB1vYE+jVToJOKGzqyxMU2gbaNIIQ2jatGt02N8TVTMEeWvpfcif8/gDYq6OsIbdq0SSNGjFBiYqJCQkL01ltv+Y0bY5STk6PLL79cUVFRSktL0+eff+5Xc+TIEY0dO1ZOp1OxsbGaMGGCjh/3/6vsk08+0XXXXafIyEglJSVp7ty5Z/SycuVK9e7dW5GRkerfv79Wr14ddC+wh7qrRp9r4UPOfuqOLK26f2i9C2doAW1b0EGovLxcAwYM0IIFC846PnfuXD377LNavHixtmzZog4dOsjtduvEiRNWzdixY/Xpp59q3bp1WrVqlTZt2qR7773XGvf5fLrpppvUvXt3FRYW6sknn9QjjzyiF154warZvHmzxowZowkTJmj79u0aOXKkRo4cqR07dgTVCwD76hIbVW9AJiQDNmAugCTz5ptvWo9ra2tNQkKCefLJJ611ZWVlxuFwmKVLlxpjjNm5c6eRZD788EOr5p133jEhISHm4MGDxhhjFi5caDp27GgqKyutmhkzZphevXpZj2+//XaTnp7u109qaqq57777Au6lIV6v10gyXq83oHq0PMUHykz3GatM8YGy5m4FuGD8/wwEJpjP70a9xcbevXvl8XiUlpZmrYuJiVFqaqoKCgokSQUFBYqNjdWQIUOsmrS0NIWGhmrLli1WzfXXX6+IiP+bvOp2u7V7924dPXrUqjl1P3U1dfsJpJfTVVZWyufz+S0AAKDtatQg5PF4JEnx8fF+6+Pj460xj8ejuLg4v/F27dqpU6dOfjVn28ap+zhXzanjDfVyutzcXMXExFhLUlJSAD81AABorbjp6ilmzpwpr9drLfv372/ulgAAwEXUqEEoISFBklRSUuK3vqSkxBpLSEhQaWmp3/jJkyd15MgRv5qzbePUfZyr5tTxhno5ncPhkNPp9FsAAEDb1ahBKDk5WQkJCcrPz7fW+Xw+bdmyRS6XS5LkcrlUVlamwsJCq2b9+vWqra1VamqqVbNp0yZVV1dbNevWrVOvXr3UsWNHq+bU/dTV1O0nkF7Q+h0sq9COg95zLlw1GgBQn6AvqHj8+HHt2bPHerx3714VFRWpU6dO6tatm7KysvSb3/xGPXv2VHJysh5++GElJiZq5MiRkqSrrrpKN998s+655x4tXrxY1dXVmjJliu644w4lJiZKku68807NmTNHEyZM0IwZM7Rjxw7Nnz9fzzzzjLXfBx54QMOGDdNTTz2l9PR0LVu2TB999JF1in1ISEiDvaB146rRAIALFuwpaX/729+MpDOWjIwMY8y/T1t/+OGHTXx8vHE4HObGG280u3fv9tvGN998Y8aMGWOio6ON0+k048aNM8eOHfOr+fjjj83QoUONw+EwXbp0MU888cQZvaxYscJceeWVJiIiwvTt29fk5eX5jQfSS304fb5lqzuV+M1tB0zxgbJzLgeOftvcrQKNgtPngcAE8/kdYowxzZjDWjSfz6eYmBh5vV7mCzWDQG6WmrW8SKvuH6p+XWKasDOgeew46NWtz73H//NAA4L5/OZeY2iR+NoLANAUCEJokbhZKgCgKRCE0KLV3SwVAICLgQsqAgAA2+KIEBpdQ5OcJb7SAi5EQ9fH4vcLCBxBCI0qmEnO7z44jH+sgSB07BChqPAwZS0vqreO3y8gcAQhNKpAJjnXnfZ+tLyKf6iBIHSJjdK7Dw4L6LIS/H4BgSEI4aIIZJJzfYf3uTUGcHZdYqMCCjiN8TvEV2ywA4IQmlwwh/e5RhAQnEB/vwLBV2ywA4IQmlwgh/cl/hoFzkegv18N4Ss22AVBCM0i0MP7AILH7xcQOIIQAOCcAplrxNFbtGYEIQDAGYKZa8RcIrRmBCEAwBkCnWvEXCK0dgQhAMBZMdcIdsC9xgAAgG1xRAgA0CJwn0I0B4IQLIH8I9QQrggN4Hxwn0I0F4KQTTQUcr4pr9Kk/1fY4D9CgeCK0ACCxX0K0VwIQjYQzF9ar45PUecLDDEcugZwvgK5TyHQmAhCNhDIX1oSAQbAxdPQUWm+VkdzIQjZCH9pAWgOwRyV5mt1NDWCEADggtV3RGdP6XGOSqPFIggBAM5boLfiiAoP0/eSOxF00OIQhAAA5y3QW3FwtActFUEIAHBBuBUHWjOCUDNqrKuocjYGAADnhyDUTBrrKqqcjQEAwPkjCDWTxrqKKtcIAmA3jXGUuzGOtge6HbRsBKFm1ljX9uEaQQDaukDPUAtEYx5t595nrRtBqBVo6PocAGAHgZ6h1pDGOtrOvc/aBoJQCxbM9TmY/wPADhrzDLVA/sjkaHvbZ4sgtGDBAj355JPyeDwaMGCAnnvuOaWkpDR3Ww3i+hwA0Pj4IxOnavNBaPny5crOztbixYuVmpqqefPmye12a/fu3YqLi2vu9hrE9TkAoHHZ/Y9MJoH7a/NB6Omnn9Y999yjcePGSZIWL16svLw8/eEPf9BDDz3UzN0BAJpDS/sjM5Bw0hi+Ka/SpP9X2GSTwFtD6GrTQaiqqkqFhYWaOXOmtS40NFRpaWkqKCg4o76yslKVlZXWY6/XK0ny+XyN3tvxYz7VVn6r48d88vlCGn37AICLq+7f8U+++ErHj53/58SRb6uVtWy7TlTXNmJ35xYZHqqFd1yjTu3Dzzr+xeFyPfRGsTYW/0vfuazDee8n0J8rMjxUf5kyVImNGIbqPreNMQ0Xmzbs4MGDRpLZvHmz3/pp06aZlJSUM+pnz55tJLGwsLCwsLC0gWX//v0NZoU2fUQoWDNnzlR2drb1uLa2VkeOHFHnzp0VEsJRm0D5fD4lJSVp//79cjqdzd0O6sF71TrwPrUevFctgzFGx44dU2JiYoO1bToIXXrppQoLC1NJSYnf+pKSEiUkJJxR73A45HA4/NbFxsZezBbbNKfTyT8ErQTvVevA+9R68F41v5iYmIDqQi9yH80qIiJCgwcPVn5+vrWutrZW+fn5crlczdgZAABoCdr0ESFJys7OVkZGhoYMGaKUlBTNmzdP5eXl1llkAADAvtp8EBo9erQOHz6snJwceTweDRw4UGvWrFF8fHxzt9ZmORwOzZ49+4yvGdHy8F61DrxPrQfvVesTYkwg55YBAAC0PW16jhAAAEB9CEIAAMC2CEIAAMC2CEIAAMC2CEI4LwsWLNAVV1yhyMhIpaamauvWrfXWz5s3T7169VJUVJSSkpI0depUnThxoom6tadNmzZpxIgRSkxMVEhIiN56660Gn7NhwwYNGjRIDodDPXr00JIlSy56nwj+vXrjjTf0ox/9SJdddpmcTqdcLpfWrl3bNM3a3Pn8XtV5//331a5dOw0cOPCi9YfgEYQQtOXLlys7O1uzZ8/Wtm3bNGDAALndbpWWlp61/vXXX9dDDz2k2bNna9euXXr55Ze1fPly/epXv2rizu2lvLxcAwYM0IIFCwKq37t3r9LT0zV8+HAVFRUpKytLEydO5AO2CQT7Xm3atEk/+tGPtHr1ahUWFmr48OEaMWKEtm/ffpE7RbDvVZ2ysjL97Gc/04033niROsP54vR5BC01NVXf+9739Pzzz0v699W6k5KSdP/99+uhhx46o37KlCnatWuX3xW+H3zwQW3ZskXvvfdek/VtZyEhIXrzzTc1cuTIc9bMmDFDeXl52rFjh7XujjvuUFlZmdasWdMEXUIK7L06m759+2r06NHKycm5OI3hDMG8V3fccYd69uypsLAwvfXWWyoqKrro/SEwHBFCUKqqqlRYWKi0tDRrXWhoqNLS0lRQUHDW53z/+99XYWGh9fXZF198odWrV+s//uM/mqRnBKagoMDvfZUkt9t9zvcVLUdtba2OHTumTp06NXcrOItXXnlFX3zxhWbPnt3creAs2vyVpdG4vv76a9XU1JxxZe74+Hj94x//OOtz7rzzTn399dcaOnSojDE6efKkJk2axFdjLYzH4znr++rz+VRRUaGoqKhm6gwN+d3vfqfjx4/r9ttvb+5WcJrPP/9cDz30kP7+97+rXTs+clsijgjhotuwYYN++9vfauHChdq2bZveeOMN5eXl6bHHHmvu1oBW7/XXX9ecOXO0YsUKxcXFNXc7OEVNTY3uvPNOzZkzR1deeWVzt4NzIJ4iKJdeeqnCwsJUUlLit76kpEQJCQlnfc7DDz+su+66SxMnTpQk9e/fX+Xl5br33nv161//WqGh5PGWICEh4azvq9Pp5GhQC7Vs2TJNnDhRK1euPONrTTS/Y8eO6aOPPtL27ds1ZcoUSf/+GtMYo3bt2umvf/2rfvjDHzZzl+ATCEGJiIjQ4MGD/SY+19bWKj8/Xy6X66zP+fbbb88IO2FhYZIk5uq3HC6Xy+99laR169ad831F81q6dKnGjRunpUuXKj09vbnbwVk4nU4VFxerqKjIWiZNmqRevXqpqKhIqampzd0ixBEhnIfs7GxlZGRoyJAhSklJ0bx581ReXq5x48ZJkn72s5+pS5cuys3NlSSNGDFCTz/9tK655hqlpqZqz549evjhhzVixAgrEKHxHT9+XHv27LEe7927V0VFRerUqZO6deummTNn6uDBg/rjH/8oSZo0aZKef/55TZ8+XePHj9f69eu1YsUK5eXlNdePYBvBvlevv/66MjIyNH/+fKWmpsrj8UiSoqKiFBMT0yw/g10E816FhoaqX79+fs+Pi4tTZGTkGevRjAxwHp577jnTrVs3ExERYVJSUswHH3xgjQ0bNsxkZGRYj6urq80jjzxivvvd75rIyEiTlJRkfv7zn5ujR482feM28re//c1IOmOpe28yMjLMsGHDznjOwIEDTUREhPnOd75jXnnllSbv246Cfa+GDRtWbz0unvP5vTrV7NmzzYABA5qkVwSG6wgBAADbYo4QAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwrf8Pbh5XyysYCSMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is [2970, 2971, 2972, 2973, 2974]\n",
      "2970 1.007991220482554\n",
      "2971 0.9792568969197366\n",
      "2972 1.0263836851372932\n",
      "2973 1.0210383326789645\n",
      "2974 1.0076939548176553\n",
      "[2970, 2971, 2972, 2973, 2974] {2970, 2971, 2972, 2973, 2974}\n"
     ]
    }
   ],
   "source": [
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "#print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()\n",
    "\n",
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "barredJettisonSet = list()\n",
    "for u in range(nUnits):\n",
    "    if abs(allUnits[u] - int(allUnits[u]) - 0.25) < 0.01:\n",
    "        barredJettisonSet.append(u)\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)\n",
    "for u in barredJettisonSet:\n",
    "    print(u,unitUse[u])\n",
    "\n",
    "origBarredJettisonSet = set(list(barredJettisonSet))\n",
    "for u in origBarredJettisonSet:\n",
    "    if unitUse[u] > 1.04:\n",
    "        barredJettisonSet.remove(u)  #this one is overused in this state\n",
    "print(barredJettisonSet, origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "31823195-d300-4938-9178-0c83d17a00e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 3049 overPopped HDs to within 1551\n",
      "working on squaring down HD 3 time is now 0\n",
      "working on squaring down HD 115 time is now 13\n",
      "working on squaring down HD 271 time is now 47\n",
      "working on squaring down HD 431 time is now 403\n",
      "working on squaring down HD 634 time is now 425\n",
      "working on squaring down HD 826 time is now 438\n",
      "working on squaring down HD 936 time is now 453\n",
      "working on squaring down HD 1051 time is now 461\n",
      "working on squaring down HD 1185 time is now 487\n",
      "working on squaring down HD 1290 time is now 501\n",
      "working on squaring down HD 1461 time is now 551\n",
      "working on squaring down HD 1626 time is now 563\n",
      "working on squaring down HD 1735 time is now 584\n",
      "working on squaring down HD 1828 time is now 594\n",
      "working on squaring down HD 1925 time is now 601\n",
      "working on squaring down HD 2042 time is now 611\n",
      "working on squaring down HD 2148 time is now 632\n",
      "working on squaring down HD 2237 time is now 648\n",
      "working on squaring down HD 2316 time is now 662\n",
      "working on squaring down HD 2452 time is now 670\n",
      "working on squaring down HD 2530 time is now 677\n",
      "working on squaring down HD 2625 time is now 692\n",
      "working on squaring down HD 2730 time is now 702\n",
      "working on squaring down HD 2851 time is now 715\n",
      "working on squaring down HD 2954 time is now 770\n",
      "working on squaring down HD 3109 time is now 781\n",
      "working on squaring down HD 3229 time is now 790\n",
      "working on squaring down HD 3342 time is now 796\n",
      "working on squaring down HD 3417 time is now 803\n",
      "working on squaring down HD 3524 time is now 815\n",
      "working on squaring down HD 3644 time is now 822\n",
      "working on squaring down HD 3755 time is now 831\n",
      "working on squaring down HD 3862 time is now 847\n",
      "working on squaring down HD 4003 time is now 861\n",
      "working on squaring down HD 4106 time is now 871\n",
      "working on squaring down HD 4264 time is now 926\n",
      "working on squaring down HD 4382 time is now 951\n",
      "working on squaring down HD 4563 time is now 974\n",
      "working on squaring down HD 4693 time is now 979\n",
      "working on squaring down HD 4916 time is now 1000\n",
      "working on squaring down HD 5149 time is now 1028\n",
      "working on squaring down HD 5439 time is now 1262\n",
      "working on squaring down HD 5710 time is now 1402\n",
      "working on squaring down HD 5828 time is now 1412\n",
      "working on squaring down HD 5980 time is now 1442\n",
      "working on squaring down HD 6109 time is now 1475\n",
      "working on squaring down HD 6346 time is now 1514\n",
      "working on squaring down HD 6523 time is now 1591\n",
      "working on squaring down HD 6699 time is now 1729\n",
      "working on squaring down HD 6857 time is now 1748\n",
      "working on squaring down HD 6952 time is now 1754\n",
      "We have addressed overpop in a total of 3049 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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Wr732Gp555hnjcw0YMAAbNmzAq6++ir/+9a/o0qULtm7ditjYWOMYQ8L2M888g9LSUtx7773IzMyEUilNro6/j2cW8S69VYMjF6wLAv+x/zz6RYVwuZmISAbc/fi/QbPqCLk7R9YR+vsPZ7Hkm1NND3RTXgqgqaLRhnyifS8Pdct/bERErsLV+0KKUkeIbNOupXOSsF2FNZ0zWF2aiEh6ntYXkoGQSK5XWFdQkFyjSR8RkTvyxL6QDIREUnyzWuopuAxWlyYikoYn9oW0u7I02UZbzlWOprBSKxGRtKxdkXenlXuuCIkkPNhf6inIirlUaAHA/DRWaiUikoq1K/LutHLPQEgk/TpxlUOB2lMHKyc0LGdvsHjHCbdLxCMichVNNaI1vI+708o9AyGR/HrlptRTkJyhn9iIXhrMT+thdoy7nkogInIFhka0gOf0WGMgJJLz1xkIzU7uguGxGuj0AhbvOGl2jLueSiAichWWGtF+NKmPS9QRsgWTpUWitZCF7ymuV1Qj5+x16AXB6lMJ7D1GROQclipHe1KPNQZCIrnmRhn29lqXcwHrci6gtb+PVePd6VQCEbkmZ7SZkEPrCmsqRzfWiNbdMBASScG1CqmnIBult2usGudOpxKIyPU4o82EHFpXGCpH108+MORouuP2lyXMERLJHea72EQd5OdWpxKIyLU4o82EHFpXeGLl6KYwEBKJvy8X32wxvl9Ht9yLJiL5c0awIJcAxBMrRzeFgZBIPL3pqq0i2wZKPQUi8lDOCBbkEoB4YuXopjAQEkmAn7fUU3ApzA8iIqk4I1iQSwDiiZWjm8JASCQRwQFST8EluGPVUiJyLc4IFuQSgHhi5eimMBASyWN9IqSeguy5a9VSInItzggW5BKAeGLl6KYwEBLJgC5tpZ6CbKiD/DB9UBQ0HlK1lIhcizOCBTkFIJ5WObopCkEQPOeMnI3Ky8uhUqlQVlaGoKCgZl/vsY/246cLpc2fmItppWyBG5V3jL+rg5RY+FCMx1QtJSLX5K51hAzkUNjRWWz5/GYgZIGjA6FeCzNRXqlzwMzcw+zkrpg5tLPb/MMjIvdhCBK0ZbdRXFGNkJZ+UAe5T2Vpd2fL5zeL24jkZuUdBkH1LMv+FRsOXsCiUXd73FIsEcmXpVUbRwQsntK6wlUwR0gkszYdkXoKsnT5RhWeXX8EXx+/JPVUiIhkUf2ZxMVASCSnim5KPQVZm7HxKL4+zjcYIpKOXKo/k7gYCImkhTf3fy0RBOAvG/hti4ikI5fqzyQuBkIi6Rne/GRrV9I6wAetA3warZnRGH7bIiKpyKX6M4mLgZBIOoR4VmXpsls1KL1VY3aJ2RJ+2yIiqcil+jOJi4GQSIL9faWegqgE1BYJCw7wQXCAj02P5bctIpKCXKo/k7gYCImk+Fa11FMQnQCg5FYNPni8NwJtaDp7/lqF8yZFRNQIMao/6/QCcs5ex5fHLiLn7HWmAsgA6wiJ5OeLZVJPQTLFt6rx7pg4PLveuhICGw8VYubQLiwwRkSiM7SfqF9HSO2A6s9yqiotBlcpHMlASCS3q+80PchNhbZSIim6DVZN6oMXP/sPKqotF5bUllfhUEExC44RkSSGx2oc3gLIUJ+o/vqPoT6Ru/X4cqWgj1tjIqm645lVpVsH+Bj304fHavD6wz2tehzzhIhISobqz6Pi2yMpuk2zt8M8qT6RqxWlZCAkkuo7eqmnIIkpA6Lg7aUw7oufvXLDqsfxVAYRuQtr6xOt3V/g8rlDrhj0cWtMJEH+vgBuST0NUbUO8MHMoZ3NLpE2RoHavXieyiAiV2fIkfnGyhWQxTtOGv+3XLeRmmJLUUq5pD8wEBJJSkwYcgtLpZ6GqN58pCeyTmitTpJ21KkMIiKp2fIF0BxXzR1yxaKU3BoTyd3hKqmnIJqwVr5YNakPUmLUmPvFz1Y/Tq1Sutw/eiKi+hrLkbGFXLeRmuKKRSm5IiQST6oj9NR90Rgeq8H72fkovVXT5PiWft54+r5ozBzamStBROTSLOXI2EqO20hNMRSl1JZVmv0byDH9gStCImnb0k/qKYhm35mr0OkFZOwvsGr8zSodlmf/iqwTWifPjIjIuZrKkbGHnLaRmiJGUUpHYyAkEr0LLW021+GCYhw4cw2lt5teDarL1ZaAiYjqszZo+XNSJ8xP62HVWDltI1nDUJRSrTKdt1zTH7g1JpKcs9elnoJobtXo8dS6wzY9xhWXgImI6rN29f/BWA36RYVg9b4Cl9pGspYzilI6CwMhkfzn91KppyCqqjv2rey40hIwEVFdmXlFWPjVLxbH1A1uDNtIz60/AgVgEgzJdRvJFoailHLHrTGRKFu45n/IYnO1JWAiIuCPk2La8qpGx5gLblxtG8kdcUVIJJ5ZV9p6rrwETESezdqTYo01bnWlbSR3xEBIJCUedHzeVu6wBExEnsvak2JLH4vDwC5tzd7nKttI7simrbHIyEgoFIoGPzNmzAAATJ8+HdHR0fD390e7du0watQonDp1yvj4tWvXmn28QqHAlStXAADff/+92fu1WtOj1R9++CEiIyOhVCqRmJiIQ4cOmdxfWVmJGTNmoE2bNmjZsiUeffRRXL582a4/kiP8XnxbsueWi0A/b/SJUCE4wMfkdi4BE5Ersza38VpF49tmJB2bVoQOHz4Mne6PLup5eXlISUnBmDFjAAAJCQmYOHEiOnbsiOLiYixcuBDDhg1DQUEBvL29MW7cOAwfPtzkmk8++SQqKysRGhpqcvvp06cRFBRk/L3u/Zs2bUJ6ejpWrVqFxMRELF++HKmpqTh9+rRx3OzZs7Fjxw5s3rwZKpUKM2fOxCOPPIL9+/fb8pId5o7eNTbHvBWAzkkn2CuqdDjyWxkAICTQBw/Ht0dyjJpLwETk0lyxmjL9QSEIgt0fe7NmzcL27duRn58PhaLhB9nx48cRFxeHM2fOIDo6usH9V69eRfv27bFmzRo88cQTAGpXhIYMGYKSkhK0bt3a7PMmJiaib9++WLFiBQBAr9cjIiICzz//PObOnYuysjK0a9cOGzZswGOPPQYAOHXqFHr06IGcnBz079/fqtdXXl4OlUqFsrIyk6DMHve+uQu/l/JElIHhv5YPJ/RBcKAv98WJyGXp9ALufWt3k8fg9708FACYCyQCWz6/7c4Rqq6uxvr165Genm42CKqoqEBGRgaioqIQERFh9hrr1q1DQECAMVipKz4+HlVVVYiNjcXChQsxcOBA4/Pm5uZi3rx5xrFeXl5ITk5GTk4OACA3Nxc1NTVITk42junevTs6duxoMRCqqqpCVdUfS5fl5eVW/CWsEx/RGr+XsnKygeHNYubGI6hbQ9FVOy4Tkeey9hh81gltg0asfM+Tnt3H57du3YrS0lI8+eSTJrevXLkSLVu2RMuWLfHNN98gKysLvr6+Zq+xZs0aTJgwAf7+/sbbNBoNVq1ahX//+9/497//jYiICNx///04cqS2g/m1a9eg0+kQFhZmcq2wsDBjHpFWq4Wvr2+DFaW6Y8xZsmQJVCqV8aexAM4esR7UdNUW9QtJGzouZ+YVSTMhIiI7NHUMHoDZRqx8z5Oe3StCa9aswYMPPojw8HCT2ydOnIiUlBQUFRVh6dKlGDt2LPbv3w+l0vQ/jpycHJw8eRL/+te/TG7v1q0bunXrZvx9wIABOHv2LJYtW9ZgrKPNmzcP6enpxt/Ly8sdFgyVV95xyHXcnYDab1CLtp1ASoyaS8ZE5DIaOwYPAPe+tdvsthnf86RnVyB04cIFZGdn44svvmhwn2E1pUuXLujfvz+Cg4OxZcsWjB8/3mTc6tWrER8fj4SEhCafr1+/fti3bx8AoG3btvD29m5wAuzy5ctQq9UAALVajerqapSWlpqsCtUdY46fnx/8/JzTHPVSmXufGqu/HNwcbLdBRK7K3DH4nLPXLR6v53uetOzaGsvIyEBoaCjS0tIsjhMEAYIgmOTdAMDNmzfx2WefYdq0aVY937Fjx6DR1O6f+vr6IiEhAbt27TLer9frsWvXLiQlJQGoPb3m4+NjMub06dMoLCw0jhFbmMq9u88746CZXNtt6PQCcs5ex5fHLiLn7HU2iiUii6x9L5Pre567s3lFSK/XIyMjA5MnT0aLFn88/Ny5c9i0aROGDRuGdu3a4ffff8ebb74Jf39/jBgxwuQamzZtwp07dzBp0qQG11++fDmioqJw9913o7KyEqtXr8bu3buxc+dO45j09HRMnjwZ99xzD/r164fly5ejoqICU6ZMAVC7KjVt2jSkp6cjJCQEQUFBeP7555GUlGT1iTFHu3nLfbfGfL0VqHbCmXs5HjXNzCtisiMR2YTH6+XN5kAoOzsbhYWFmDp1qsntSqUSe/fuxfLly1FSUoKwsDAMGjQIBw4caFAjaM2aNXjkkUfMHo+vrq7Giy++iIsXLyIgIAC9evVCdnY2hgwZYhwzbtw4XL16Fa+99hq0Wi3i4+ORmZlpkkC9bNkyeHl54dFHH0VVVRVSU1OxcuVKW1+uw/xedkuy53Y2ZwRBAFAis+Jjhl5C9V+tIdmRRSGJyJx+USHQqJRu2WXeHTSrjpC7c2Qdod5/+xYlbrwq5Aya/9bdkEPyoKFOSGP7/HXrhMhhvkQkL4YvUoD54/X8IuVYtnx+s/u8SGruuEZlaTkxJA/KQVO9hOomOxIR1ccu8/LFpqsi8WnhDVQzGLKVXJIHmexIRM3FLvPyxEBIJJ2ClSi5VSP1NFyOXJIHmexIRI7ALvPyw60xkQQofZoeREYK1OYIySV50JDs2Nj3NrnNl4iIrMNASCQh/gyEbLVgZIxslowNvYQANAiG6vYSkst8iUj+WJNMHrg1JpKC6+57fN7R1EF+WPjQ3bJLHjQkO9avI6RmHSEishFrkskHAyGRVFTz6Lw1Zid3xcyhnWW7ssJkRyJqLtYkkxcGQiQLrvRNiMmORGQvnV7Aom0n2IBVRhgIiSSqbSDOX3fvxqu2mp/WA21b+XFVhYjckk4vNFg9tqUmGb9wiYOBkEjqF9HyZIYqzE8OjGLwQ0RuqbEcoBGxaqseL+eaZOYCPHveyx11neZiICSS1v7u3X3eWjxhRUTuzlIO0Jr95626hlxrkjkqyVtOyeI8Pi8Sd/3Qb9HI62rp1wJDu7dDSKCvye0sJ09E7qypHCAA8FI0LMNhIOeaZIYAr/7WniHJOzOvSNTrOApXhESSFN0GK747I/U0HO7Of+teBPp5425NEPpGhWBAdFv0v6sNvL0Usln6JCISQ1M5QABgKBekgPkGrHJcMXdUkrcck8W5IiSSvpHyi+4d6VaVDofPl6BnexUGdm5r/A/YcMJqVHx7JEW3kd0/biIiR7I2t2fqwEiXasDqiMbTOr2AtfsLZNfAmitCIsm9UCL1FJzKEN3z2CcRuRNbV7Wtze1JiVHjlbQYl1kxb27jaXM5QY54PkdgICQSOZ8AcKSiskqs2J2PF5K7Sj0VIqJmsSeh19CXUFtWaXb7x3Bq1hD0uMoR+eY0nm4sedwRz+cI3BoTSdtAzzk1tiw7X/RkNyKi5qjf9+vr4/Yl9LprX0J7G09bygmy5TrOxBUhkdzR6aWegqi4RUZErsLcyo+XAlYn9NbfPkuJUbtdX0JDgPfc+iM2JXlbkzxuzXWciYGQSLYcvSj1FETFyqhE5Aoa27ax1Ai+bkJv2e3qRrfP9r081GVygKxhT+NpW9JCpAoUGQiJ5PdSz+s+X/cfAI/RE5Hc2LptU1/WCS0y9p/3qOaptjaetjbXZ35aD8m6DTAQEommtT9woVTqaYjK8A9AThVEiYgMbNm2MWfrsUuyqocjFluSvK1NHpey5RKTpUUSowmSegqiMiS7ya2CKBGRgb2neRUAQgJ9UFxR3egYKerhyJErJI8zEBLJVQ85Pm+wYGQMdHoBf93ys8VS84u2nYDO0mY8EZGT2HNE2/Bx/XB8e6vGe0rpFEsMuUVyLSDJrTGRbDt2SeopiGb2f2sI9V+yC8UVNY2Oq/uNiUnVRCS2prZtgNrTY3W/qxkSelX+vlY1UJVr81Sx2ZpbJCYGQiIpvd14QOBublbV2FQ8i9+YiEgK1hwJXzG+N4ID/Rp8eOv0gtWFE6mWXAtIcmtMJN5envOnXrOvQLYVRImI6mpq22ZEr3Cz/RKlzn2pXwCSKQb244qQSFr6eaHyjmcUVbT23yO/MRGRHNi7bWNPXR1rWSo5wpO4jsVASCQBvi2AijtST0N2pD4tQEQE2L9tY0sQZW09NUuBDgCzqQfuXLvI2RgIiaTaQ1aDrBUS6IM3Hu7Jf7BE5PKsCaKsXcVprNK1tqwSz64/gtYBPh5Zu8iZPCdxRWLBgb5ST0E22gT64sd5yQyCiEi2HJmDY209NUuVrg23ld6y7iQuWY8rQiJRtvC8mLOxUxj/+3AsfD3w70FErsEROTiGbTBt2W0s3nHSqlWc5la6NuBJXNswEBKLwrOWKWcnd8WnhwsbJBDOT6utv/HlsYuyqiNBRARY3pqyNgfHXCDVmLqrOI4KYHgS1zYMhETiSZ/1rf1b4J7IYHQM8UdxRTVCWvpBHaRESUUVFu/gSQcikqemtqasycFpLJBqiiGBujl4Etc+DIRE0j0sCLmFZVJPQxRllXcwcfVB4+8alRIPxWnw8Z6G9YV40oGI5KKprammquE3p5u9YYW8qSKNqgAflP03T8hc6gFP4tqOiRoi6d0pWOopiEao9y9YW1aJv5sJggD2HCMi+bB2a6qxcfbk+CjwR5Nqa4o0vvlIT1n37XJFXBESSZkHtdior6nwhj3HiEgOrN2aamycrTk+5lZxrC3SKNe+Xa6IgZBIWvv7SD0F2eNJByKSkjVbU5ZycGzN8WmsArU1RRrl2rfLFTEQEsm1iiqppyB7POlARFKypgmrpRwca7rZhwT6YP6f7oY6yPIqDgMd8TBHSCQni25IPQXZqrtHTkQkpaaasFrKwWkqx0cB4I2He+Lh3qYNXElaXBESya1qndRTkCWedCAiubG3CavhsU3l+Fjbc4zEwUBIJH0jQ7DzxGWppyE7jujSTETkaM3ZmrIUSLFzvPwoBKH+YWcyKC8vh0qlQllZGYKCgpp1rdvVOvR4LdNBM3MP89N64MmBUfwmREQeobFii4Z3QB5/dxxbPr+ZIySSY7+VSj0F2Wnbyo9BEBF5BGsaqrKemjQYCImER8Mb4ikxIvIUtlStJnHZFAhFRkZCoVA0+JkxYwYAYPr06YiOjoa/vz/atWuHUaNG4dSpU8bHr1271uzjFQoFrly5Yhz3/fffo0+fPvDz80Pnzp2xdu3aBnP58MMPERkZCaVSicTERBw6dMjk/srKSsyYMQNt2rRBy5Yt8eijj+LyZelydPihb4qnxIjIkzS3ajU5j02B0OHDh1FUVGT8ycrKAgCMGTMGAJCQkICMjAycPHkS3377LQRBwLBhw6DT1Z6YGjdunMnji4qKkJqaisGDByM0NBQAUFBQgLS0NAwZMgTHjh3DrFmz8NRTT+Hbb781zmPTpk1IT0/HggULcOTIEcTFxSE1NdUkmJo9eza2bduGzZs344cffsClS5fwyCOPNO+v1QwJHtRiwxoPxWng7aWATi8g5+x1fHnsInLOXueyMBG5peZWrSbnaVay9KxZs7B9+3bk5+dDoWiY63H8+HHExcXhzJkziI6ObnD/1atX0b59e6xZswZPPPEEAODll1/Gjh07kJeXZxz3+OOPo7S0FJmZtcnGiYmJ6Nu3L1asWAEA0Ov1iIiIwPPPP4+5c+eirKwM7dq1w4YNG/DYY48BAE6dOoUePXogJycH/fv3t+r1OTJZev+ZayaNSD2dRqXE/LQYdqMnItlw5rF2nV7AvW/tbrJq9b6XhzJ30gFESZaurq7G+vXrMXXqVLNBUEVFBTIyMhAVFYWIiAiz11i3bh0CAgKMwQoA5OTkIDk52WRcamoqcnJyjM+bm5trMsbLywvJycnGMbm5uaipqTEZ0717d3Ts2NE4xpyqqiqUl5eb/DjKgTPXHHYtd1BUVom/bDjSYM/c0I0+M69IopkRkSfKzCvCvW/txvhPfsQLnx7D+E9+xL1v7XbYe5E1DVVZT00adgdCW7duRWlpKZ588kmT21euXImWLVuiZcuW+Oabb5CVlQVfX1+z11izZg0mTJgAf39/421arRZhYWEm48LCwlBeXo7bt2/j2rVr0Ol0ZsdotVrjNXx9fdG6detGx5izZMkSqFQq409jAZw9fiu55bBruTOeniAisRmOtTv7i1lzqlaT89hdUHHNmjV48MEHER4ebnL7xIkTkZKSgqKiIixduhRjx47F/v37oVSa/j8+JycHJ0+exL/+9S97p+Bw8+bNQ3p6uvH38vJyhwVD12+w15i12I2eiMTS1LF2BWq/mKXEqB2yWtOcqtXkHHYFQhcuXEB2dja++OKLBvcZVlO6dOmC/v37Izg4GFu2bMH48eNNxq1evRrx8fFISEgwuV2tVjc43XX58mUEBQXB398f3t7e8Pb2NjtGrVYbr1FdXY3S0lKTVaG6Y8zx8/ODn5+fVX8DWwUoWcTbVjw9QUTOZsuxdkd9MWNDVXmxa2ssIyMDoaGhSEtLszhOEAQIgoCqKtPVkJs3b+Kzzz7DtGnTGjwmKSkJu3btMrktKysLSUlJAABfX18kJCSYjNHr9di1a5dxTEJCAnx8fEzGnD59GoWFhcYxYusXyf/ogYZ745bw9AQRORuPtZPNgZBer0dGRgYmT56MFi3+WOU4d+4clixZgtzcXBQWFuLAgQMYM2YM/P39MWLECJNrbNq0CXfu3MGkSZMaXP/ZZ5/FuXPnMGfOHJw6dQorV67EZ599htmzZxvHpKen45NPPsE///lPnDx5Es899xwqKiowZcoUALWrUtOmTUN6ejq+++475ObmYsqUKUhKSrL6xJijjb3HcflGrqT+aq9apcTKCb2hUSkbDYrYjZ6IxMJj7WTzfk12djYKCwsxdepUk9uVSiX27t2L5cuXo6SkBGFhYRg0aBAOHDhgrBFksGbNGjzyyCMNkpkBICoqCjt27MDs2bPx/vvvo0OHDli9ejVSU1ONY8aNG4erV6/itddeg1arRXx8PDIzM00SqJctWwYvLy88+uijqKqqQmpqKlauXGnry3WYd7491fQgN2IIclaM74PgQN8Ge+FeXgo8t/4IFIDJ3jxPTxCRmPpFhUCjUjZ6rB1w7Bczdp6XHzZdtcCRdYT+9MFe5F1y3HF8ubOmHhC7MBORHDTWDNVg+qAozBsR45Dn4XueOGz5/GYgZAEDIfutnNAbqbGaJr/58NsREcnBkq9P4O97Cszep0DzO8Oz87y4bPn85lEmkUS1CfCoQOj/fX4cf9t+Etpyy998eHqCiKRWfUePzbkXLY5pzhF6sY/ok23YfV4kikYXXd1TRbXOJAgCWDWaiOQnM68I/Zdko7iiutExze0Mz87z8sZASCTXb92RegqSY9VoIpITw3ZVcUWNVePtPULPI/ryxkBIJP4+/FMD/OZDRPJgabuqMfYeoecRfXnjp7NIQoP4H3hd/OZDRFJqaruqrubWNjMc0WftNHliICSSIH8fqacgK/zmQ0RSsvXLWHNqm7HzvLwxEBKJtpQrIAC/+RCRPFj7ZaxNoK9Djraz87x88fi8SAQPOzVmDr/5EJFcWFNROiTQBznzHoBvC8esGbDzvDxxRUgk9b8FuIvH+nSAOsjP5DZ1kB+mD4qCht98iEimmtquUgB44+GeDguC6j5vUnQbjIpvj6ToNrIJgnR6ATlnr+PLYxeRc/a6R53s5YqQSG5Wuufx+VZKb5h7G+ndMRhzhvfgNx8iki3DdlX9thdqidteiF1x39Nbf7DFhgWObLHx1D8PI/vkFQfNTN5YMp6IXImcWv2IHZS4a+sPWz6/uTUmktvV7rci1NjbBAsnEpErEXu7qrFtKENQUv9Yv7Oq8jfV+gPwjPdxbo2JJDjQV+opOJylfxp1CyeylxgRUa3GVnzmp8Vg8Q5x+5HZ0vrDnd/HuSIkkhZe7vWnDvT1tmocCycSEdWytOLzlw0Nb6/LGVX52fqjlnt9OsuY2o0qSysAPDPoLqvGsnAiEZF121DWcGRQwtYftRgIieRGlXVN/eTOUFxs5tAuLBlPRGQlW1p6WOLIoIStP2oxEBKJQuH6x8a9FcC+l4dieKyGJeOJiGzQ3JUcZwQlfB+vxUBIJJ1CAqSeQrPpBGDI0u+NJxdYMp6IyDq2rOSIGZTwfZynxkTTNayV1FNwCG157THOWcldEdk2AKGtlPjh/w1B7oUSWdTgICKSo6ZaeihQG3zMT+uBxTtOilrg0dNbfzAQEslBB2b6S00AsCz7V+PvIYE+eH1ULEbFt5duUkREMmbYhnpu/REoYJogXXfFZ3isBqmxGtGDEkMtJU/ErTGRXCq9LfUUnKa4ogZ/2XAUS74+IfVUiIhky9ptKLn2I3NXXBESiZsX5gQA/H1PAeI6BGNEL/ffUyYisoenb0PJEQMhkXjKf+KvfpmH1FjHVT4lInI3nrwNJUfcGhNJ+2D3LkhlUFxRjRW786WeBhERkVUYCIkkMdJzov9l2fkObw5IRETkDAyERPLrlZtST0FUdTsWN9ZpmYiISGrMERLJbyW3pJ6CqAzNActuV5vttOzMmhhERFLT6QUmRLsIBkIiiQj2l3oKotv5SxHWHrjQoHiYtqy2KKOnVC0lIs+SmVfEL4AuhFtjIumuDpJ6CqL7/MhFi52W626fERG5g8y8Ijy3/kiDBquGL4DMn5QfBkIiKb5VLfUURHej8k6j9wmo3T5blnWaeUNE5BZ0egGLtp3gF0AXw0BIJCEBvlJPQZZWfHcW4z/5Efe+tZvflIjIpR0qKG6wElSX4Qvg2v0FDIZkhIGQSE5pb0g9BYfy97H8n05LP2+brsdlYyJydVduNB4E1bV4x0l++ZMRBkIicbdTY+P7dTRbLVvx3x9bT0dw2ZiIXF1oK+sL5/LLn3wwEBJJp5AAqafgUCp/H3w0qQ80ZpoHzkruirLbjecHNcawbHyooNhBsyQiEk+/qBBoVEqrWirxy598MBASyRNJkVJPwaGWZde20dj38lBsfLo/3n88Hhuf7o99Lw9FZNvmBX3WLi8TEcmJt5cCC0bGALCuvyS//MkDAyGR+LbwQruW7pUwvWjbCQBAUnQbjIpvj6ToNvD2Uti0PGxOcx9PRCSV4bEafDSpD9Qq69/H+OVPWgyERKLTC6jWudfyZ2PfZGxZHq5LgdqiY/2iQhwyPyIiKQyP1WDfy0MxP62HVeP55U9aDIREUttuokbqaTicuW8yti4P1x23YGQMy9ATkcvz9lLgyYFRFr8U8sufPDAQEom7Ln2a+yaj0wtQ+ftiysBIBAeabgdqVEpMHxRlNsmaLTeIyJ1Y+lLIL3/ywV5jInHHgormvsmY67ETEuiDh+PbIzlGbWw8OGd4DzYkJCK3Z8gZqv++qGbvMdlgICQSdyuoCDT8JmPosVM/E6qkogb/2H8efesEO95eCiRFtxFxtkRE0hgeq0FKjJpf/mSKgZBI3K2gYku/FkiJURt/t7bHTkqMmv/4icjj8MuffNmUIxQZGQmFQtHgZ8aMGQCA6dOnIzo6Gv7+/mjXrh1GjRqFU6dONbjO2rVr0atXLyiVSoSGhhofDwDnz583+xw//vijyTU2b96M7t27Q6lUomfPnvj6669N7hcEAa+99ho0Gg38/f2RnJyM/Px8W16uQ0UEu1dBxZtVd3CooBg6vYCcs9exLOtXiz12ANbLICIyMLx3fnnsIhtPS8ymFaHDhw9Dp9MZf8/Ly0NKSgrGjBkDAEhISMDEiRPRsWNHFBcXY+HChRg2bBgKCgrg7V3be+q9997Du+++i3feeQeJiYmoqKjA+fPnGzxXdnY27r77buPvbdr8EUkfOHAA48ePx5IlS/CnP/0JGzZswOjRo3HkyBHExsYCAN5++2188MEH+Oc//4moqCjMnz8fqampOHHiBJRK8Y8qdg1tKfpzOlvWCS3SPzvWZABUl7bcPZPGiYislZlXhIVf/QJteZXxNnWQHxY+dDdzhiSgEATB7jB01qxZ2L59O/Lz86FQNNzuOH78OOLi4nDmzBlER0ejpKQE7du3x7Zt2/DAAw+Yveb58+cRFRWFo0ePIj4+3uyYcePGoaKiAtu3bzfe1r9/f8THx2PVqlUQBAHh4eF48cUX8dJLLwEAysrKEBYWhrVr1+Lxxx+36vWVl5dDpVKhrKwMQUFBVj2mMUu/PY0V351p1jXcwfy0Hph2311ST4OISBKZeUV4dv2RRu9fxdOzDmHL57fdx+erq6uxfv16TJ061WwQVFFRgYyMDERFRSEiIgIAkJWVBb1ej4sXL6JHjx7o0KEDxo4di99++63B4x966CGEhobi3nvvxVdffWVyX05ODpKTk01uS01NRU5ODgCgoKAAWq3WZIxKpUJiYqJxjDlVVVUoLy83+XEUnV7vsGu5spBA9zs9R0RkDZ1ewNwvfrY4Zu4XP3ObTGR2B0Jbt25FaWkpnnzySZPbV65ciZYtW6Jly5b45ptvkJWVBV/f2g+/c+fOQa/X44033sDy5cvx+eefo7i4GCkpKaiurgYAtGzZEu+++y42b96MHTt24N5778Xo0aNNgiGtVouwsDCT5w0LC4NWqzXeb7itsTHmLFmyBCqVyvhjCOAcofSW+xVTtIda5S/1FIiInMZS7s+PZ683+VlQeqsGP5697uxpUh12nxpbs2YNHnzwQYSHh5vcPnHiRKSkpKCoqAhLly7F2LFjsX//fiiVSuj1etTU1OCDDz7AsGHDAAAbN26EWq3Gd999h9TUVLRt2xbp6enG6/Xt2xeXLl3CO++8g4ceesje6Vpl3rx5Js9dXl7usGAo/7L7HZ+3FSuoEpE7M1dHTVOnXlDOuWtWXSfn3DUM7NLWWdOkeuxaEbpw4QKys7Px1FNPNbhPpVKhS5cuGDRoED7//HOcOnUKW7ZsAQBoNLX7njExMcbx7dq1Q9u2bVFYWNjo8yUmJuLMmT/ya9RqNS5fvmwy5vLly1Cr1cb7Dbc1NsYcPz8/BAUFmfw4jIefGFeAFVSJyH0Z6qjVPzyiLavEc+uPIDOvCLY3HSIx2BUIZWRkIDQ0FGlpaRbHCYIAQRBQVVWbGT9w4EAAwOnTp41jiouLce3aNXTq1KnR6xw7dswYRAFAUlISdu3aZTImKysLSUlJAICoqCio1WqTMeXl5Th48KBxjNjq1tzxRKoAH6mnQETkFNbWUUu0ckWc9YbEZfPWmF6vR0ZGBiZPnowWLf54+Llz57Bp0yYMGzYM7dq1w++//44333wT/v7+GDFiBACga9euGDVqFF544QV8/PHHCAoKwrx589C9e3cMGTIEAPDPf/4Tvr6+6N27NwDgiy++wD/+8Q+sXr3a+FwvvPACBg8ejHfffRdpaWn49NNP8dNPP+Hjjz8GACgUCsyaNQuvv/46unTpYjw+Hx4ejtGjR9v9x2qOKQOjsOSbhjWVPEXZrRo8t/4I+4kRkds5VFBssYyIgNo6al4KBVoH+FjMEwoO8EH/uxgIicnmFaHs7GwUFhZi6tSpJrcrlUrs3bsXI0aMQOfOnTFu3Di0atUKBw4cQGhoqHHcunXrkJiYiLS0NAwePBg+Pj7IzMyEj88fKwaLFy9GQkICEhMT8eWXX2LTpk2YMmWK8f4BAwZgw4YN+PjjjxEXF4fPP/8cW7duNdYQAoA5c+bg+eefxzPPPIO+ffvi5s2byMzMlKSGEAD4tvBCpxD3SxSuf2CwsZ2vut+KeCKCiNyJtU21r1VU4c1Helocs+SRnkwhEFmz6gi5O0fWEQKA4cv3uGXPsdnJXRDZNhDXblRh8Y6TTY7f+HR/Lv0SkdvIOXsd4z/5sclxhve+2oKKJ0wKzGrYhNWhbPn8Zq8xkej0As5dvSn1NBxOAeDTw79h38tDsf34JaseY+23JyIiV9AvKgQalRLaskqzeUIK1HabN5yaZRNWeWEgJJIfz11Htc79Ft8Me9+HCopx/pp1jWVDW0mzPUlE5AzeXgosGBmD59YfgQIwCYYMoU39U7NswiofdhdUJNscOGtd/QhXpS27jY2HGi+BYKAO8mMtISJyO8NjNfhoUh+oVaZf9NQqJQ+JyBxXhETye7F1qyWuqrii2qqGquP7deTyLxG5JW55uSYGQtQshr3vkJZ+Vo2PbBvo3AkREUmIW16uh4GQSDoEB0g9BadZMDIGKn/rmqkyP4iIiOSEOUIiGdDZ/frGeCmAZwZFYXisxnhqorEFYAXYa4yIiOSHgZBI+ka6XwAgCMDHewqQmVcEby8FXnmwe6NHRwH2GiMiIvnh1phIci+USD0FhxNQG+Qs2nYCuRdKsGZfgdlxahYKIyIimWIgJBJ3LSJoqCP0yV7zQRAA/KmXhkEQERHJErfGRNI20LpTVVJRtvCCXwvn/OewZl8Bqu/onXJtIiKi5mAgJBaZp8ZU3tGjyknBil4A/vrFcadcm4iIqDkYCInk2s0qqacgqc+PXERmXpHU0yAiIjLBQEgkrJ9Tm1St07tfvzUi8kw6vYCcs9fx5bGLyDl7ne9vLorJ0iJJ6BQMhaL2yLmnMjRnZdVVInJ1mXlFWLTtBIrK/jgIo+EJWZfEFSGR5F4o8eggyMBdT88RkefIzCvCc+uPmARBAKAtq8Rz648wDcDFMBASCQOAWnI/PUdEZIlOL2DRthNmi8cabmMagGthICQS5gjVenHzf/htiYhc1qGC4gYrQXUZaqsdKigWb1LULAyERJLQKVjuJ+hNqPxbQB3k+ODtcjmXjonIdVm7us9dANfBQEgkuRdKzC6lytXjfSNQeUfn8Oty6ZiIXJm1q/vcBXAdDIRE4krfDpQ+Xvj7ngKU3qpxyvW5dExErqpfVAg0KmWjK/wK1J4e6xflfo223RUDIZG4UpJwZY047TBcKTgkIgIAby8FFoyMAdCwYYDh9wUjY+Dt5UrJEJ6NgZBI9G54dr65/8y5dExErmh4rAYfTeoDtcr0PUytUuKjSX1YR8jFsKCiSA662TbQg7Fq5Jy9jtLbtm+fKVD7hsGlYyJyVcNjNUiJUeNQQTGu3KhEaKva97TGVoJ0esHqsc4kl3nICQMhkdzROz7xWErf5GmN/zvQzxsVVda9Pi4dE5G78PZSWFUpXy5VqOUyD7nh1phIbla6VyBUl7VBEMClYyLyLHKpQi2XecgRV4RE4n4ZQtZRoPa1Tx0YiZQYNZdhichjNFWFWoHaUiIpMWqnvi/KZR5yxRUhkXjgf1sAAFWAD1ZN6oPXRt6NpOg2HvmPjIg8k1yqUMtlHnLFQEgkcR1aSz0FSfj7eCMlRi31NIiIRCeXKtRymYdcMRASyfWKaqmnIAlP/pZBRJ5NLlWo5TIPuWIgJJITl8qlnoJkPPVbBhF5NrlUoZbLPOSKgZBIbtfckXoKkgltpYROLyDn7HV8eewics5eZ58xInJ7cqlCLZd5yBVPjYmkb2Qb7DxxReppiMpQOLGkohr3vrWbtSuIyOMYqlDXr9+jFvk9UC7zkCOFILhh7wcHKS8vh0qlQllZGYKCgpp1reKb1ejzepaDZiY/hmPydX8HgGcGReHjPQUNjm0a7mdNISLyBHKp6CyXeTibLZ/fXBESyZx/H5N6Cg5XN9j56j9FDb5lzE/rgcU7TrJ2BRF5PGurUHvKPOSEgZBITmtvOu3aPt4K1OjEX9iru6Q6Z3iPBt8ybKldwX+YREQkBQZCIlH5++C3kttOuXaNTkBIoC+KnXhEX/PfFZ7gQD+zS6rmvmWwdgUREckdAyGRvJjcFVPW/eS06z8Up8HaAxcccq3aoCcGwYG+du8j6/QCrt2osmqsp9auICIi6TEQEsmg7qENEood6fPciwj09UZFtWkD1NYBPhh3Twd8vKcAsPD8IYE+eDi+PZId0A/MXIdjcwynyjy1dgUREUmPgZBIvL0U6BrWEqcvOydX6GaV+TpFZbdq0LtjMD6aFNwgOGkT6ItR8eEObYZq6HDcVMDH2hVERCQHDIRE1DHE32mBkCWLtp3AvpeHIiVG3SChGahtyLf9+CWzW2C2HLW01OG4PtauICIiOWAgJKJ+UW2RdfKqqM9Z/2RW3YRmc1tYdQsdNnV/fU2dEjOYn9YDTw6M4koQERFJji02RDR5QKRkz73/zDWTthaGLaz6gYu2rBLPrT+CJV+fsHh/Zl5Rg+ew9vRX21Z+DIKIiEgWbAqEIiMjoVAoGvzMmDEDADB9+nRER0fD398f7dq1w6hRo3Dq1KkG11m7di169eoFpVKJ0NBQ4+MNjh8/jvvuuw9KpRIRERF4++23G1xj8+bN6N69O5RKJXr27Imvv/7a5H5BEPDaa69Bo9HA398fycnJyM/Pt+XlOpxvCy88fV+UJM+94rszuPet3cjMK7K4hWW47ZO9DatB171/0bYTDfqFscMxERG5GpsCocOHD6OoqMj4k5VV2zJizJgxAICEhARkZGTg5MmT+PbbbyEIAoYNGwad7o+TTO+99x5eeeUVzJ07F7/88guys7ORmppqvL+8vBzDhg1Dp06dkJubi3feeQcLFy7Exx9/bBxz4MABjB8/HtOmTcPRo0cxevRojB49Gnl5ecYxb7/9Nj744AOsWrUKBw8eRGBgIFJTU1FZKW3NmqHdwyR7bsNqzord+U0WOrTUE7Xudltd7HBMRESuplm9xmbNmoXt27cjPz8fCkXDj7/jx48jLi4OZ86cQXR0NEpKStC+fXts27YNDzzwgNlrfvTRR3jllVeg1Wrh6+sLAJg7dy62bt1qXF0aN24cKioqsH37duPj+vfvj/j4eKxatQqCICA8PBwvvvgiXnrpJQBAWVkZwsLCsHbtWjz++ONWvT5H9hoz2HL0ImZvOuaQa9lDAcDf1xu36h2zt8f7j8djVHx7k9sMW26A+d5j7C1GRETOZsvnt905QtXV1Vi/fj2mTp1qNgiqqKhARkYGoqKiEBERAQDIysqCXq/HxYsX0aNHD3To0AFjx47Fb7/9ZnxcTk4OBg0aZAyCACA1NRWnT59GSUmJcUxycrLJ86WmpiInJwcAUFBQAK1WazJGpVIhMTHROMacqqoqlJeXm/w42v58cZOl6xMAhwRBgPktLkOHY7XK9D61SskgiIiIZMfuU2Nbt25FaWkpnnzySZPbV65ciTlz5qCiogLdunVDVlaWMag5d+4c9Ho93njjDbz//vtQqVR49dVXkZKSguPHj8PX1xdarRZRUaZ5NGFhtdtJWq0WwcHB0Gq1xtvqjtFqtcZxdR9nbow5S5YswaJFi2z/Y1hJpxeQdfKK067vSJaKPzZVCHF4rMbsUX0mSBMRkdzYvSK0Zs0aPPjggwgPDze5feLEiTh69Ch++OEHdO3aFWPHjjXm5ej1etTU1OCDDz5Aamoq+vfvj40bNyI/Px/fffdd816JA8ybNw9lZWXGn7orVY5wqKAYZbdrHHpNZxnavR0UQIN8H2sLIRp6j42Kb4+k6DYMgoiISJbsCoQuXLiA7OxsPPXUUw3uU6lU6NKlCwYNGoTPP/8cp06dwpYtWwAAGk3ttkhMTIxxfLt27dC2bVsUFhYCANRqNS5fvmxyTcPvarXa4pi699d9nLkx5vj5+SEoKMjkx5FcqbnoU/dF46NJfRAW5Gdye1iQH7e4iIjIbdgVCGVkZCA0NBRpaWkWxwmCAEEQUFVV23xz4MCBAIDTp08bxxQXF+PatWvo1KkTACApKQl79uxBTc0fKydZWVno1q0bgoODjWN27dpl8lxZWVlISkoCAERFRUGtVpuMKS8vx8GDB41jpHD+WoVkz22rkgpDw9TG1oSIiIhcn82BkF6vR0ZGBiZPnowWLf5IMTp37hyWLFmC3NxcFBYW4sCBAxgzZgz8/f0xYsQIAEDXrl0xatQovPDCCzhw4ADy8vIwefJkdO/eHUOGDAEATJgwAb6+vpg2bRp++eUXbNq0Ce+//z7S09ONz/XCCy8gMzMT7777Lk6dOoWFCxfip59+wsyZMwEACoUCs2bNwuuvv46vvvoKP//8M/785z8jPDwco0ePbs7fy246vYANBx3THV4Mf92ah+fWH4G23HQV63J54wUViYiIXI3NgVB2djYKCwsxdepUk9uVSiX27t2LESNGoHPnzhg3bhxatWqFAwcOIDQ01Dhu3bp1SExMRFpaGgYPHgwfHx9kZmbCx8cHQO3W2s6dO1FQUICEhAS8+OKLeO211/DMM88YrzFgwABs2LABH3/8MeLi4vD5559j69atiI2NNY6ZM2cOnn/+eTzzzDPo27cvbt68iczMTCiV0hTzO1RQjMs3qiV5bnuU3qqxuaAiERGRq2lWHSF358g6QluO/I7Zn/3HQTOTh41P9zfpXUZERCQHotQRItsUV7jOapC1XCn5m4iIyBwGQiIJaenX9CAXw55hRETk6hgIiUQd5FpBQ6CvN3uGERGR22MgJBJDQ1JX4eNdGwbZW1CRiIjIFTAQEom3lwILRsY0PVAmSm/fwazkruwZRkREbs3uXmNku5QYNQJ8vXCrWi/1VKwS2TYA+14earZnmE4vsJcYERG5PAZCIlqx+4zLBEFAbTK0oWdYXZl5RVi07QSKyv44NaZRKbFgZAxXioiIyKVwa0wkOr2Af+wvkHoaVlP5tzCbDJ2ZV4Tn1h8xCYIAQFvGitNEROR6GAiJxJU6zwPA1IFRDba6dHoBi7adYMVpIiJyGwyERFK/Z5ecBQf4YObQLg1uP1RQ3GAlqC4BQFFZJQ4VFDtxdkRERI7DQEgkxTermh4kMnOpzQoASx7paTbx2dpK0vvPXOWqEBERuQQGQiIJCfR1yHUcdS5r6sDIBkfjNSolPpzQGyp/X3x57CJyzl43CWisrSS94ruzuPet3cwXIiIi2eOpMZGoVf4Ouo4SD8Vp8PGeArO5OtZKiVHjlbQYkyPwJRXVWLyj8dNghqKQ2rLKJp/bkDzNmkNERCRnXBESiaMqSy99LA7zRsTgmUFRdl/DSwEkdAo2Ho0fFd8eZberMWOD5dNgdYtCNrUyxeRpIiJyBQyEROLtpcD8tB7Nvs63J7TYn38NXx6zf9tJLwC5F0qMv9tyGmx4rAYfTerTYFvNHCZPExGR3DEQElFwYPM70K/LuYCJaw42+xRa3cRnW0+DDY/VYN/LQzFzSGebn4uIiEhOGAiJSE4BQd3EZ2vnVXect5cCAzu3tfm5iIiI5ISBkIjkEBAoUJsAXbdqtLXzqj/OkPfUWL6QueciIiKSEwZCIuoXFQJ1kLTBkABgflqMSZ0gewMaS8nTht8XjIxhM1YiIpItBkIi8vZSYHy/jlJPA4t3nDCp8dOcgKax5Gm1Ssmj80REJHsKQRB4trkR5eXlUKlUKCsrQ1BQkEOu+eWxi3jh02MOuZa9DOFM/UClOV3ldXrBpCZRv6gQrgQREZEkbPn8ZkFFkTkjT+jRPu3x7yMXrR4voDYYWrTtBFJi1MaAZXisBikxarsCGkNNIiIiIlfCQEhktXlCftCWN957TAFYXTU6OMAHbz8Wh44hgViW/avV86h7JL5uAMOAhoiInEluOwgMhESWdUKLkls1FsfYslf5v6N74lBBMTqG+CM4wKfJa9cnpyP9RETk3pqTguEsDIRElJlXhOfWH2lWjzCD1v4+GNe3Q4PeYLaSw5F+IiJyf419Bkrdm5KnxkRiqY2FPZ4c0Akf7yloVhAEACUV1Q6aERERkXm2tHISGwMhkTTVxsJahpo+nx7+zSFB1eIdbIpKRETOZWsrJzExEBKJI3JxDKlkj/ftaDHZ2hZsikpERM5mTysnsTBHSCSOyMVR/zehrOqO3gEz+gMTpomIyBr2nviyt5WTGBgIicTQXsOervEqZQusnJSAvpEhyL1Qgv1nrjl0bkyYJiKipjTnxJehlZO2rNJsWocCtV/2pehNya0xkdR2a7evPk9Z5R38dL4Yg9/5DuM/+RErvjvjkDmxKSoREVnDcOKrfp6P4cRX3bZN5si5NyUDIRHdqLxj92OXZec7JNm6LgG1+UZERESNcdSJL7n2puTWmIgCfb2lnkIDy7J/xaeHCyUtZkVERPJly4mvpjoTNKeVk7MwEBLR6N7tseXYJdGft3WAD55MigQgYPmuhttqRWWVeHb9EQzu0hYRIQEIVHrjSlkVwlv7Y0B0W/SPbsMGqkREHsrRJ77k1sqJgZCIWniJtxPZ0s8b3l4KlN2+g9JbNVi+Kx9NxTI/5DdMwv7w+7NoHeCDNx/pyRUjIiIPJOcTX47AHCERXatwTO0fSwyxzs0qHcpum+Yk2Vs3sfRWDZ61IhmOiIjcj+HEV2PfpV394A0DIRGJES2rVUq0DvBxyrUXfvULq1ATEXkYOZ/4cgQGQiIyRNWO9lif9lg2Ng4bn+6PpY/FodTGDvTW0pZXsQo1EZEHkuuJL0dgjpCIvL0UeChOg7/vKXDodT8/chH7z153StXp+liFmojIM8nxxJcjMBASkU4v4Kv/OCfPxlDUalZyV6dc3+D8tQqnXp+IiORLbie+HIFbYyJyVAd6cwyZO58eLoQ6yK/RpDYATZ4es2RZdj6TpomIyG0wEBKRs7eVDEWtxverrRZtLqlNAWDF+D7Y+HR/DO3ezubnUMC6CqJERESugIGQiMSqsRDZNtBiUtuIXhokRbfBP57sh+mDomxaIapbQZSIiMjV2RQIRUZGQqFQNPiZMWMGAGD69OmIjo6Gv78/2rVrh1GjRuHUqVMm1zD3+E8//dR4//fff292jFarNbnOhx9+iMjISCiVSiQmJuLQoUMm91dWVmLGjBlo06YNWrZsiUcffRSXL1+26Y/jaE3VYnCU0FZKDI/VYN/LQ7Hx6f54//F4bHy6P/a9PLRBZv+8ETE4tfhBzE/rgSf6d8LAaOvqQDBpmoiI3IFNgdDhw4dRVFRk/MnKygIAjBkzBgCQkJCAjIwMnDx5Et9++y0EQcCwYcOg0+lMrpORkWFyndGjRzd4rtOnT5uMCQ0NNd63adMmpKenY8GCBThy5Aji4uKQmpqKK1euGMfMnj0b27Ztw+bNm/HDDz/g0qVLeOSRR2x5uQ5nqRaDI9QvamVIahsV396Y3JZz9jq+PHYROWevG7e3fFt4Ydp9d2Hx6FjMHGpdsrWrVhAlIiKqSyEIgt3JHrNmzcL27duRn58PhaLhR/vx48cRFxeHM2fOIDo6uvYJFQps2bLFbPAD1K4IDRkyBCUlJWjdurXZMYmJiejbty9WrFgBANDr9YiIiMDzzz+PuXPnoqysDO3atcOGDRvw2GOPAQBOnTqFHj16ICcnB/3797fq9ZWXl0OlUqGsrAxBQUFWPcYamXlFWLTthFWJ0y39vHGzStfkOKA2EGqsnoO559SolA2arer0Au59aze0ZZVmOw0rULvFtu/loS5/ZJKIiNyTLZ/fducIVVdXY/369Zg6darZIKiiogIZGRmIiopCRESEyX0zZsxA27Zt0a9fP/zjH/+AuVgsPj4eGo0GKSkp2L9/v8nz5ubmIjk5+Y8X4eWF5ORk5OTkAAByc3NRU1NjMqZ79+7o2LGjcYw5VVVVKC8vN/lxhuGxGsxP64GQwMYrQBsSm5++L9qqa4YE+lgMgp5bf6RB4KX9b7PV97N/Na4SAXDrCqJERER12V1HaOvWrSgtLcWTTz5pcvvKlSsxZ84cVFRUoFu3bsjKyoKvr6/x/r/97W8YOnQoAgICsHPnTvzlL3/BzZs38T//8z8AAI1Gg1WrVuGee+5BVVUVVq9ejfvvvx8HDx5Enz59cO3aNeh0OoSFhZk8b1hYmDEfSavVwtfXt8GKUlhYWINco7qWLFmCRYsW2fsnsVpmXhFmbDhqdsXFQP3f1ZqUGDU+PVzY6AoNALT0a4Ef5yXDt0XDuFanF7Bo2wmzjzXctiw733ibYZXoo0l9Gqwgqc2sIBEREbkyuwOhNWvW4MEHH0R4eLjJ7RMnTkRKSgqKioqwdOlSjB07Fvv374dSWZtTMn/+fOPY3r17o6KiAu+8844xEOrWrRu6detmHDNgwACcPXsWy5Ytw7/+9S97p2uVefPmIT093fh7eXl5g9Ws5rIUmBiEBPrgh/83xBjYLBgZg2fXH2l0/M2qO9h96rLZAMXW2kWGwowfTeqDfS8PdbsKokRERHXZtTV24cIFZGdn46mnnmpwn0qlQpcuXTBo0CB8/vnnOHXqFLZs2dLotRITE/H777+jqqrxzuz9+vXDmTNnAABt27aFt7d3gxNgly9fhlqtBgCo1WpUV1ejtLS00THm+Pn5ISgoyOTH0awJTIorapB7ocT4e0qM2mIjVUu1fWw93WW4wqJtJwDAJNmaQRAREbkbuwKhjIwMhIaGIi0tzeI4QRAgCILFIOfYsWMIDg6Gn5+fxTEaTe1qh6+vLxISErBr1y7j/Xq9Hrt27UJSUhKA2tNrPj4+JmNOnz6NwsJC4xipWBuYGMbp9ALW7i+w2EjVUm0fe053sVYQERF5Cpu3xvR6PTIyMjB58mS0aPHHw8+dO4dNmzZh2LBhaNeuHX7//Xe8+eab8Pf3x4gRIwAA27Ztw+XLl9G/f38olUpkZWXhjTfewEsvvWS8zvLlyxEVFYW7774blZWVWL16NXbv3o2dO3cax6Snp2Py5Mm455570K9fPyxfvhwVFRWYMmUKgNpVqWnTpiE9PR0hISEICgrC888/j6SkJKtPjDmLtYFJaCulTafLAPNBlqF2kaUcI1uuR0RE5E5sDoSys7NRWFiIqVOnmtyuVCqxd+9eLF++HCUlJQgLC8OgQYNw4MABYw0gHx8ffPjhh5g9ezYEQUDnzp3x3nvv4emnnzZep7q6Gi+++CIuXryIgIAA9OrVC9nZ2RgyZIhxzLhx43D16lW89tpr0Gq1iI+PR2ZmpkkC9bJly+Dl5YVHH30UVVVVSE1NxcqVK23+AzlaU4GJ4Xh6SUU1Zmw4YlPw0jaw4aqaoXaRpRyjxrBWEBERubtm1RFyd86sI/TcfwOTun98QwbOhxN6Y/GOkzY3aFX5+2DqwEjMHNrFJJ9HpxeQ8HqWxe21ulgriIiIXJkodYTIfsNjNXhmUJTZ+54ZFIXgQD+7utSX3a7Bsux89Fr0Lb4+fsl4+6GCYpuCIIC1goiIyDPYfXye7JeZV4S/7ylocLsA4O97CnBH17xFuooqHf6y4Sim/16KeSNibMr1UauUmJ8WA5W/L7Yc+R3FFdUIaekHddAfrTt4pJ6IiNwFAyGR6fQC5n7xs8Uxn/70m0Oe6+97ChDXIdjqXJ/5aT2gUSmxeIf5BG3DEf66q0vm2nQQERG5Cm6NiezHs9eb3KaqqNIhJNDXIY1Z53+Zh4ROwRa73huatWpUSszYcLTRbbnSWzUN5m4owJiZV+SA2RIREYmLgZDIDpy9ZtW4jiH+Nh93N+d6RTVyL5Q02T9sfloMFu84afNz1i3AaK6gIxERkZwxEBLZ4fPWFSk89ltZo/eZ6ylmyZUblRgeq8FHk/pArTLdJlOrlPhoUh8EB/ralaANsAAjERG5LuYIiUinF/Cf30ubfZ1AX29U39FbPd6QIzQ8VoOUGDV+PHf9v53mBSTd1Rb9o9tge51TZvZiAUYiInI1DIREdKigGFV3mr99VHKrBrOTu+Af+wtQdvuOxbEa1R+nvQAg64TWpFr1iu/OQqNS4vG+HZs9LxZgJCIiV8OtMRE5csUksm0gjswfhj/1avy0lgKm9YAMhRzrb4FpyyqxPPtXi41dmxIc4GMScBEREbkCBkIicuSKSWgrJby9FFgxoQ9WTuiDkEBfk/s1/839MRxr1+kFLNp2wmwytOG25pxSY5o0ERG5Im6NiahfVAjUQX7QllfZfQ1D+4u6qy8jemmQGqu2WOjwUEGxxWRoAX9suX16+DebE6dLb9XgUEExkqLb2PqSiIiIJMNASETeXgosfOhuuxqgApbbX3h7KSwGIdZuy0W2DcS+l4fiUEExtGW3UVxRjfPXK/CvHwubfCyTpYmIyNUwEBLZ8FgNnkzqhLU5F5ocq/L3QdntPwoYqutUcdbpBZtaXVi7LWfYcqsbVOWcvW5VIMRkaSIicjUMhESWmVeEfx/93aqxM4d0Rmx7VYNgJzOvyOTkF9B0q4t+USHQqJTQllWazecxt+XmiMcSERHJGZOlRWQ4tXWjUmfV+LYtfZEU3Qaj4tsjKbqNMQhq7OSXpVYX3l6KJqtLN9Zx3trH6vQC1uw9h9e+zMOavedsqnVEREQkBQZCIrF0aqsxapW/1dewptVFU9WlLTVObeqxRwtL0H3+N1i84yTW5VzA4h0n0X3+N1jy9YkmXycREZFUuDUmkqZObdVXvxCiNdeo2+qiscRpQ3VpW/KLmnrs25kn8fc9BQ3G6wUYb583IqbJ6xMREYmNgZBIbDlRVb8QooG27LZDnqupE2a2PLb6jh6f7G0YBNX1yd4CvDisu8090oiIiJyNn0wiseVE1azkrg22qTLzirB4x0mHP1dz/SvnPJpqOq8XascRERHJDVeERNIvKgSt/X1QWuc4fGMi2waY/G5IkG4qv0iK01sXim85dBwREZGYuCIkEm8vBaYMjLRqbN0VHVuTrBs7+eUsnUICmh5kwzgiIiIxMRAS0cyhXSw2NlWgYZK0tUnWIYE+TZ78coYnkiLRVNzlpagdR0REJDcMhETk7aXAm4/0bPR+AQ1XdKxNsp7/p7tFD4IAwLeFF56+L8rimKfvi2KiNBERyRJzhGRMpxdw7YZ1DVrVQdK1tzAcjf9kb4FJ4rSXojYI4tF5IiKSKwZCIjLk+1iy8KtfkBKjRtYJbYM2Go0xV3NIbPNGxODFYd3xr5zzuFB8C51CAvBEUiRXgoiISNYYCInImnwfbXkVXvj0KHYcL7I6Qfp2jQ5ZJ7SSbI3V5dvCC9Puu0vSORAREdmCX9dFZG1BxO02BEEAUHarxmKfMSIiIjKPgZCIiiuqnXJda/qMERERUUMMhEQU0tLPadeu22eMiIiIrMNASERinOyypacZERGRp2MgJKJ+USHQqJwbDInZZ4yIiMjVMRASkbeXAgtGxsDaBhi2NMowV5WaiIiILGMgJLLhsRp8NKlPo602FP/9mT4oCup6q0eGx9QPkAy/i91njIiIyNUpBEHgMaNGlJeXQ6VSoaysDEFBQQ69tk4vYMXufGTsP2/SkV6jUmLByBgMj9VApxdwqKAYV25UIrRV7WqPuUKLdR9DRETk6Wz5/GYgZIEzAyEDc8FOU6s69jyGiIjIU9jy+c3K0hLz9lIgKbqN0x9DREREDTFHiIiIiDwWAyEiIiLyWAyEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg8FitLW2DoPlJeXi7xTIiIiMhahs9ta7qIMRCy4MaNGwCAiIgIiWdCREREtrpx4wZUKpXFMWy6aoFer8elS5fQqlUrKBTu2dS0vLwcERER+O2335zWWFZqfI3uwxNeJ1+je/CE1wjI93UKgoAbN24gPDwcXl6Ws4C4ImSBl5cXOnToIPU0RBEUFCSr/4idga/RfXjC6+RrdA+e8BoBeb7OplaCDJgsTURERB6LgRARERF5LAZCHs7Pzw8LFiyAn5+f1FNxGr5G9+EJr5Ov0T14wmsE3ON1MlmaiIiIPBZXhIiIiMhjMRAiIiIij8VAiIiIiDwWAyEiIiLyWAyEPMCHH36IyMhIKJVKJCYm4tChQxbHL1++HN26dYO/vz8iIiIwe/ZsVFZWijRb+9jyGmtqavC3v/0N0dHRUCqViIuLQ2Zmpoiztd2ePXswcuRIhIeHQ6FQYOvWrU0+5vvvv0efPn3g5+eHzp07Y+3atU6fZ3PY+hqLioowYcIEdO3aFV5eXpg1a5Yo82wOW1/jF198gZSUFLRr1w5BQUFISkrCt99+K85km8HW17lv3z4MHDgQbdq0gb+/P7p3745ly5aJM1k72fNv0mD//v1o0aIF4uPjnTY/R7D1NX7//fdQKBQNfrRarTgTthMDITe3adMmpKenY8GCBThy5Aji4uKQmpqKK1eumB2/YcMGzJ07FwsWLMDJkyexZs0abNq0CX/9619Fnrn1bH2Nr776Kv7+97/j//7v/3DixAk8++yzePjhh3H06FGRZ269iooKxMXF4cMPP7RqfEFBAdLS0jBkyBAcO3YMs2bNwlNPPSXrD1FbX2NVVRXatWuHV199FXFxcU6enWPY+hr37NmDlJQUfP3118jNzcWQIUMwcuRIWf+3Ctj+OgMDAzFz5kzs2bMHJ0+exKuvvopXX30VH3/8sZNnaj9bX6NBaWkp/vznP+OBBx5w0swcx97XePr0aRQVFRl/QkNDnTRDBxHIrfXr10+YMWOG8XedTieEh4cLS5YsMTt+xowZwtChQ01uS09PFwYOHOjUeTaHra9Ro9EIK1asMLntkUceESZOnOjUeToKAGHLli0Wx8yZM0e4++67TW4bN26ckJqa6sSZOY41r7GuwYMHCy+88ILT5uMMtr5Gg5iYGGHRokWOn5CT2Ps6H374YWHSpEmOn5AT2PIax40bJ7z66qvCggULhLi4OKfOy5GseY3fffedAEAoKSkRZU6OwhUhN1ZdXY3c3FwkJycbb/Py8kJycjJycnLMPmbAgAHIzc01bi2dO3cOX3/9NUaMGCHKnG1lz2usqqqCUqk0uc3f3x/79u1z6lzFlJOTY/I3AYDU1NRG/ybkGvR6PW7cuIGQkBCpp+JUR48exYEDBzB48GCpp+JQGRkZOHfuHBYsWCD1VJwqPj4eGo0GKSkp2L9/v9TTaRKbrrqxa9euQafTISwszOT2sLAwnDp1yuxjJkyYgGvXruHee++FIAi4c+cOnn32WdlujdnzGlNTU/Hee+9h0KBBiI6Oxq5du/DFF19Ap9OJMWVRaLVas3+T8vJy3L59G/7+/hLNjJpj6dKluHnzJsaOHSv1VJyiQ4cOuHr1Ku7cuYOFCxfiqaeeknpKDpOfn4+5c+di7969aNHCPT96NRoNVq1ahXvuuQdVVVVYvXo17r//fhw8eBB9+vSRenqN4ooQmfj+++/xxhtvYOXKlThy5Ai++OIL7NixA4sXL5Z6ag7z/vvvo0uXLujevTt8fX0xc+ZMTJkyBV5e/OdA8rVhwwYsWrQIn332mfxzLuy0d+9e/PTTT1i1ahWWL1+OjRs3Sj0lh9DpdJgwYQIWLVqErl27Sj0dp+nWrRumT5+OhIQEDBgwAP/4xz8wYMAA2Se+u2dYSgCAtm3bwtvbG5cvXza5/fLly1Cr1WYfM3/+fDzxxBPGb2I9e/ZERUUFnnnmGbzyyiuyCxbseY3t2rXD1q1bUVlZievXryM8PBxz587FXXfdJcaURaFWq83+TYKCgrga5II+/fRTPPXUU9i8eXODLU93EhUVBaD2fefy5ctYuHAhxo8fL/Gsmu/GjRv46aefcPToUcycORNA7TanIAho0aIFdu7ciaFDh0o8S+fo16+f7NMO5PWpRg7l6+uLhIQE7Nq1y3ibXq/Hrl27kJSUZPYxt27dahDseHt7AwAEGbals+c1GiiVSrRv3x537tzBv//9b4waNcrZ0xVNUlKSyd8EALKyspr8m5D8bNy4EVOmTMHGjRuRlpYm9XREo9frUVVVJfU0HCIoKAg///wzjh07Zvx59tln0a1bNxw7dgyJiYlST9Fpjh07Bo1GI/U0LOKKkJtLT0/H5MmTcc8996Bfv35Yvnw5KioqMGXKFADAn//8Z7Rv3x5LliwBAIwcORLvvfceevfujcTERJw5cwbz58/HyJEjjQGR3Nj6Gg8ePIiLFy8iPj4eFy9exMKFC6HX6zFnzhwpX4ZFN2/exJkzZ4y/FxQU4NixYwgJCUHHjh0xb948XLx4EevWrQMAPPvss1ixYgXmzJmDqVOnYvfu3fjss8+wY8cOqV5Ck2x9jUDtm6zhsVevXsWxY8fg6+uLmJgYsadvFVtf44YNGzB58mS8//77SExMNNZj8ff3h0qlkuQ1WMPW1/nhhx+iY8eO6N69O4DasgFLly7F//zP/0gyf2vY8hq9vLwQGxtr8vjQ0FAolcoGt8uJrf9/XL58OaKionD33XejsrISq1evxu7du7Fz506pXoJ1JD61RiL4v//7P6Fjx46Cr6+v0K9fP+HHH3803jd48GBh8uTJxt9ramqEhQsXCtHR0YJSqRQiIiKEv/zlL7I/DmnLa/z++++FHj16CH5+fkKbNm2EJ554Qrh48aIEs7ae4Vhq/R/D65o8ebIwePDgBo+Jj48XfH19hbvuukvIyMgQfd62sOc1mhvfqVMn0eduLVtf4+DBgy2OlytbX+cHH3wg3H333UJAQIAQFBQk9O7dW1i5cqWg0+mkeQFWsOe/17pc4fi8ra/xrbfeMn52hISECPfff7+we/duaSZvA4UgyHC/g4iIiEgEzBEiIiIij8VAiIiIiDwWAyEiIiLyWAyEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIhIdHv27MHIkSMRHh4OhUKBrVu32nwNQRCwdOlSdO3aFX5+fmjfvj3+93//16ZrsMUGERERia6iogJxcXGYOnUqHnnkEbuu8cILL2Dnzp1YunQpevbsieLiYhQXF9t0DVaWJiIiIkkpFAps2bIFo0ePNt5WVVWFV155BRs3bkRpaSliY2Px1ltv4f777wcAnDx5Er169UJeXh66detm93Nza4yIiIhkZ+bMmcjJycGnn36K48ePY8yYMRg+fDjy8/MBANu2bcNdd92F7du3IyoqCpGRkXjqqadsXhFiIERERESyUlhYiIyMDGzevBn33XcfoqOj8dJLL+Hee+9FRkYGAODcuXO4cOECNm/ejHXr1mHt2rXIzc3FY489ZtNzMUeIiIiIZOXnn3+GTqdD165dTW6vqqpCmzZtAAB6vR5VVVVYt26dcdyaNWuQkJCA06dPW71dxkCIiIiIZOXmzZvw9vZGbm4uvL29Te5r2bIlAECj0aBFixYmwVKPHj0A1K4oMRAiIiIil9S7d2/odDpcuXIF9913n9kxAwcOxJ07d3D27FlER0cDAH799VcAQKdOnax+Lp4aIyIiItHdvHkTZ86cAVAb+Lz33nsYMmQIQkJC0LFjR0yaNAn79+/Hu+++i969e+Pq1avYtWsXevXqhbS0NOj1evTt2xctW7bE8uXLodfrMWPGDAQFBWHnzp1Wz4OBEBEREYnu+++/x5AhQxrcPnnyZKxduxY1NTV4/fXXsW7dOly8eBFt27ZF//79sWjRIvTs2RMAcOnSJTz//PPYuXMnAgMD8eCDD+Ldd99FSEiI1fNgIEREREQei8fniYiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg8FgMhIiIi8lgMhIiIiMhjMRAiIiIij8VAiIiIiDwWAyEiIiLyWP8/rLl521EzAKgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN  #the barred set is dynamic; we stop shedding upon underuse. \n",
    "#   but not yet using avoidedIsland timesaver,  \n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused (new for VA)\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                #contig = avoidedIsland(tryList, unitNbrs, [unitNoToShed] )  #new for MO - faster contig check\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                #if contig:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "2109057c-54e6-4c25-bc30-d14caa29e493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.03126 0.12223\n",
      "shed unit avg and sd usage are 1.00178 0.10997\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 7059\n",
      "working on HD 600 out of 7059\n",
      "working on HD 1200 out of 7059\n",
      "working on HD 1800 out of 7059\n",
      "working on HD 2400 out of 7059\n",
      "working on HD 3000 out of 7059\n",
      "working on HD 3601 out of 7059\n",
      "working on HD 4202 out of 7059\n",
      "working on HD 4802 out of 7059\n",
      "working on HD 5402 out of 7059\n",
      "working on HD 6002 out of 7059\n",
      "working on HD 6602 out of 7059\n",
      "all done checking HD and complement contiguity for all 7059 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 1806\n",
      "overpop contigFail, enclaveFail = 0 0 out of 3049\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 2970 with pop 13737.0 now has use 1.00799\n",
      "CCB unit 2971 with pop 169151.0 now has use 0.97926\n",
      "CCB unit 2972 with pop 171003.0 now has use 1.02638\n",
      "CCB unit 2973 with pop 92501.0 now has use 1.02104\n",
      "CCB unit 2974 with pop 51938.0 now has use 1.00769\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%600 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )\n",
    "\n",
    "#NOTE - ignore the use = 1.1 stick in amped below - an artifact of use calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "7ebdd05c-eea4-4c18-b34f-41bd8e6c21fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"VRAcontigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4bb902d-5e55-429a-870d-5395f49d3c04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4c929d-0208-4785-aa15-5681a493a530",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SKIP THE BELOW IF NOT A COLD RESTART\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "e9b59389-b643-4844-9d87-d2e0bc511ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "working avg precinct-to-HDcP distance for HD 6400\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = [0.]*nUnits\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "c30cf6d4-37e3-4639-8739-549ae1673d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.067\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 781 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 200\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1231 units out of 2975 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 36835\n",
      "current avg and SD of unit usage are 1.00178 0.10997 . Now trying to increase up to 200 units' underusage\n",
      "all done trying to increase usage of unit 1936 final usage = 0.72796 21 sec elapsed 40 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.10955\n",
      "all done trying to increase usage of unit 1775 final usage = 0.98194 181 sec elapsed 369 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00181 0.10619\n",
      "all done trying to increase usage of unit 2230 final usage = 0.72768 241 sec elapsed 33 34 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00181 0.10597\n",
      "all done trying to increase usage of unit 2065 final usage = 0.98012 327 sec elapsed 229 47 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00185 0.10179\n",
      "all done trying to increase usage of unit 2250 final usage = 0.76119 398 sec elapsed 70 30 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00186 0.10108\n",
      "all done trying to increase usage of unit 118 final usage = 0.98106 529 sec elapsed 306 9 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00186 0.09827\n",
      "all done trying to increase usage of unit 1934 final usage = 0.8802 750 sec elapsed 184 118 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.00188 0.09656\n",
      "all done trying to increase usage of unit 2254 final usage = 0.7693 810 sec elapsed 40 29 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.09613\n",
      "all done trying to increase usage of unit 1946 final usage = 0.86687 985 sec elapsed 177 12 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.09496\n",
      "begin usage increase for unit 2066 with usage 0.75315 . Sec, total UU units tried = 986 10\n",
      "all done trying to increase usage of unit 2066 final usage = 0.98032 1150 sec elapsed 197 12 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.09207\n",
      "all done trying to increase usage of unit 2226 final usage = 0.90742 1319 sec elapsed 183 30 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00189 0.09071\n",
      "all done trying to increase usage of unit 1962 final usage = 0.98091 1482 sec elapsed 331 32 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.0019 0.08902\n",
      "all done trying to increase usage of unit 2084 final usage = 0.98193 1601 sec elapsed 193 6 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0019 0.0874\n",
      "all done trying to increase usage of unit 2071 final usage = 0.98171 1756 sec elapsed 191 8 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.0019 0.08604\n",
      "all done trying to increase usage of unit 1778 final usage = 0.98053 2047 sec elapsed 293 9 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0019 0.0844\n",
      "all done trying to increase usage of unit 2095 final usage = 0.98225 2197 sec elapsed 197 9 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00189 0.08321\n",
      "all done trying to increase usage of unit 2022 final usage = 0.98069 2345 sec elapsed 194 10 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.0019 0.08215\n",
      "all done trying to increase usage of unit 2242 final usage = 0.80568 2443 sec elapsed 34 51 successful, failed patches, couldn't start= 36\n",
      "current avg and SD of usage are 1.0019 0.08187\n",
      "all done trying to increase usage of unit 2262 final usage = 0.8397 2542 sec elapsed 134 33 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.00191 0.08092\n",
      "begin usage increase for unit 2080 with usage 0.78475 . Sec, total UU units tried = 2543 20\n",
      "all done trying to increase usage of unit 2080 final usage = 0.98086 2692 sec elapsed 166 50 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.0019 0.07976\n",
      "all done trying to increase usage of unit 1972 final usage = 0.9699 2871 sec elapsed 263 55 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00191 0.07839\n",
      "all done trying to increase usage of unit 2097 final usage = 0.98115 3028 sec elapsed 170 82 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00191 0.07723\n",
      "all done trying to increase usage of unit 2096 final usage = 0.96572 3223 sec elapsed 168 129 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00192 0.07636\n",
      "all done trying to increase usage of unit 1945 final usage = 0.93084 3484 sec elapsed 185 35 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00192 0.07541\n",
      "all done trying to increase usage of unit 46 final usage = 0.9365 3605 sec elapsed 173 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00194 0.07411\n",
      "all done trying to increase usage of unit 2076 final usage = 0.93865 3823 sec elapsed 129 124 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00194 0.07371\n",
      "all done trying to increase usage of unit 147 final usage = 0.98076 3963 sec elapsed 210 4 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00194 0.07232\n",
      "all done trying to increase usage of unit 59 final usage = 0.98018 4142 sec elapsed 213 5 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00194 0.07117\n",
      "all done trying to increase usage of unit 2224 final usage = 0.94557 4281 sec elapsed 175 11 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00194 0.07029\n",
      "begin usage increase for unit 2077 with usage 0.82301 . Sec, total UU units tried = 4282 30\n",
      "all done trying to increase usage of unit 2077 final usage = 0.89599 4452 sec elapsed 57 164 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00194 0.07001\n",
      "all done trying to increase usage of unit 89 final usage = 0.98038 4633 sec elapsed 187 5 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00195 0.0693\n",
      "all done trying to increase usage of unit 2341 final usage = 0.98086 4727 sec elapsed 134 15 successful, failed patches, couldn't start= 61\n",
      "current avg and SD of usage are 1.00196 0.06826\n",
      "all done trying to increase usage of unit 52 final usage = 0.98014 4873 sec elapsed 199 6 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00196 0.06764\n",
      "all done trying to increase usage of unit 2033 final usage = 0.98225 5090 sec elapsed 127 166 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00196 0.0669\n"
     ]
    }
   ],
   "source": [
    "#Now using faster avoidedEnclave, avoidedIsland subroutines vs. wontEnclave - are these buggy?\n",
    "#  updated to exclude VRA cores\n",
    "\n",
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1. or u in coreUs:  #the VRA core can't be messed with\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        complementContig = avoidedEnclave(HDunitList[t], unitNbrs, UUUc)\n",
    "        #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not complementContig: #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            complementContig  = avoidedEnclave(HDunitList[t], unitNbrs, [UUU])\n",
    "            loopUUUc = [UUU]\n",
    "        if complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in set(uuCandidates):\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop and u not in coreUs:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = avoidedEnclave(list(trySet), unitNbrs, [addU])\n",
    "                    #cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet.union(coreUset)) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap and u not in coreUs:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        cContig = avoidedIsland(list(trySet), unitNbrs, [shedU])\n",
    "                        #contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if cContig: # and contig :\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = ( set(unitNbrs[shedU]).intersection(trySet) ).difference(coreUset)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "bde6573c-b41d-4b8f-8e67-d8eced822e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00178 1.00196 and their SDs are 0.10997 0.0669\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 7059\n",
      "working on HD 500 out of 7059\n",
      "working on HD 1000 out of 7059\n",
      "working on HD 1500 out of 7059\n",
      "working on HD 2000 out of 7059\n",
      "working on HD 2500 out of 7059\n",
      "working on HD 3000 out of 7059\n",
      "working on HD 3500 out of 7059\n",
      "working on HD 4000 out of 7059\n",
      "working on HD 4500 out of 7059\n",
      "working on HD 5000 out of 7059\n",
      "working on HD 5500 out of 7059\n",
      "working on HD 6000 out of 7059\n",
      "working on HD 6500 out of 7059\n",
      "working on HD 7000 out of 7059\n",
      "all done checking HD and complement contiguity for all 7059 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"up-patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "FAILlist = list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        FAILlist.append(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"VRAunderPatched.csv\" #\"contigUnpatchedB.csv\"  #underPatched\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "1d4c25c6-0ae6-4368-a15d-72bc48b374ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04  is default maxSD.  Enter updated value\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "e.g. 0.04  0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 953 units out of 2975 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 36835\n",
      "current avg and SD of unit usage are 1.00196 0.0669 . Now trying to reduce up to 953 units' overusage\n",
      "starting to reduce usage for unit 291 with usage 1.30268 . Total units tried = 1\n",
      "try to drop unit 291 from 43 th HD.  usage down to 1.215132451196343\n",
      "try to drop unit 291 from 86 th HD.  usage down to 1.1679147186886123\n",
      "try to drop unit 291 from 129 th HD.  usage down to 1.1395781067231263\n",
      "try to drop unit 291 from 172 th HD.  usage down to 1.0954918440278294\n",
      "try to drop unit 291 from 215 th HD.  usage down to 1.0676683207442248\n",
      "all done trying to reduce usage of unit 291 final usage = 1.06697 72 sec elapsed 153 0 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.00192 0.06167\n",
      "starting to reduce usage for unit 865 with usage 1.21679 . Total units tried = 2\n",
      "try to drop unit 865 from 38 th HD.  usage down to 1.1780719742614074\n",
      "try to drop unit 865 from 76 th HD.  usage down to 1.116521693097632\n",
      "try to drop unit 865 from 114 th HD.  usage down to 1.0532393982881414\n",
      "all done trying to reduce usage of unit 865 final usage = 1.01909 109 sec elapsed 125 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00193 0.05846\n",
      "starting to reduce usage for unit 1561 with usage 1.23504 . Total units tried = 3\n",
      "try to drop unit 1561 from 45 th HD.  usage down to 1.1602428212547664\n",
      "try to drop unit 1561 from 90 th HD.  usage down to 1.0736516406112402\n",
      "all done trying to reduce usage of unit 1561 final usage = 1.01802 141 sec elapsed 102 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00191 0.05715\n",
      "starting to reduce usage for unit 1980 with usage 1.23436 . Total units tried = 4\n",
      "try to drop unit 1980 from 90 th HD.  usage down to 1.1361575155105825\n",
      "try to drop unit 1980 from 180 th HD.  usage down to 1.044955323617451\n",
      "all done trying to reduce usage of unit 1980 final usage = 1.01967 246 sec elapsed 166 0 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.00189 0.05427\n",
      "starting to reduce usage for unit 279 with usage 1.14326 . Total units tried = 5\n",
      "try to drop unit 279 from 82 th HD.  usage down to 1.1331468522925616\n",
      "try to drop unit 279 from 164 th HD.  usage down to 1.1095447729260226\n",
      "try to drop unit 279 from 246 th HD.  usage down to 1.0933376859904076\n",
      "try to drop unit 279 from 328 th HD.  usage down to 1.090131560417382\n",
      "try to drop unit 279 from 410 th HD.  usage down to 1.079527727658502\n",
      "all done trying to reduce usage of unit 279 final usage = 1.07953 283 sec elapsed 62 0 successful, failed patches, couldn't start= 351\n",
      "current avg and SD of usage are 1.00188 0.05318\n",
      "starting to reduce usage for unit 896 with usage 1.12674 . Total units tried = 6\n",
      "try to drop unit 896 from 63 th HD.  usage down to 1.0668959729664698\n",
      "all done trying to reduce usage of unit 896 final usage = 1.01994 322 sec elapsed 72 0 successful, failed patches, couldn't start= 43\n",
      "current avg and SD of usage are 1.00187 0.05186\n",
      "starting to reduce usage for unit 282 with usage 1.12821 . Total units tried = 7\n",
      "try to drop unit 282 from 90 th HD.  usage down to 1.059695085513086\n",
      "all done trying to reduce usage of unit 282 final usage = 1.01971 382 sec elapsed 83 0 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 1.00186 0.05047\n",
      "starting to reduce usage for unit 2349 with usage 1.20233 . Total units tried = 8\n",
      "try to drop unit 2349 from 80 th HD.  usage down to 1.118329719881408\n",
      "try to drop unit 2349 from 160 th HD.  usage down to 1.0538447886376665\n",
      "all done trying to reduce usage of unit 2349 final usage = 1.01766 449 sec elapsed 140 0 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.00183 0.04924\n",
      "starting to reduce usage for unit 595 with usage 1.11686 . Total units tried = 9\n",
      "try to drop unit 595 from 66 th HD.  usage down to 1.0641690016386942\n",
      "all done trying to reduce usage of unit 595 final usage = 1.0192 544 sec elapsed 77 1 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00183 0.04789\n",
      "starting to reduce usage for unit 657 with usage 1.08721 . Total units tried = 10\n",
      "try to drop unit 657 from 79 th HD.  usage down to 1.0277573511321696\n",
      "all done trying to reduce usage of unit 657 final usage = 1.01979 564 sec elapsed 45 0 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00182 0.04706\n",
      "starting to reduce usage for unit 628 with usage 1.06771 . Total units tried = 11\n",
      "all done trying to reduce usage of unit 628 final usage = 1.01921 585 sec elapsed 34 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00181 0.0467\n",
      "starting to reduce usage for unit 285 with usage 1.06379 . Total units tried = 12\n",
      "try to drop unit 285 from 72 th HD.  usage down to 1.034236113774024\n",
      "all done trying to reduce usage of unit 285 final usage = 1.01945 608 sec elapsed 31 1 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.00181 0.04634\n",
      "starting to reduce usage for unit 822 with usage 1.05845 . Total units tried = 13\n",
      "all done trying to reduce usage of unit 822 final usage = 1.01824 627 sec elapsed 28 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.0018 0.04603\n",
      "starting to reduce usage for unit 2406 with usage 1.05861 . Total units tried = 14\n",
      "try to drop unit 2406 from 59 th HD.  usage down to 1.0281333446900602\n",
      "all done trying to reduce usage of unit 2406 final usage = 1.01973 661 sec elapsed 46 0 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.0018 0.04548\n",
      "starting to reduce usage for unit 283 with usage 1.05518 . Total units tried = 15\n",
      "all done trying to reduce usage of unit 283 final usage = 1.01979 690 sec elapsed 36 0 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.0018 0.04502\n",
      "starting to reduce usage for unit 688 with usage 1.04644 . Total units tried = 16\n",
      "try to drop unit 688 from 41 th HD.  usage down to 1.0359124749436623\n",
      "all done trying to reduce usage of unit 688 final usage = 1.01951 713 sec elapsed 35 0 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00179 0.04465\n",
      "starting to reduce usage for unit 627 with usage 1.04458 . Total units tried = 17\n",
      "try to drop unit 627 from 69 th HD.  usage down to 1.0279243085604013\n",
      "all done trying to reduce usage of unit 627 final usage = 1.01942 727 sec elapsed 15 0 successful, failed patches, couldn't start= 94\n",
      "current avg and SD of usage are 1.00179 0.04443\n",
      "starting to reduce usage for unit 905 with usage 1.04165 . Total units tried = 18\n",
      "all done trying to reduce usage of unit 905 final usage = 1.01924 735 sec elapsed 8 2 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00179 0.0443\n",
      "starting to reduce usage for unit 911 with usage 1.04067 . Total units tried = 19\n",
      "try to drop unit 911 from 71 th HD.  usage down to 1.036998376915896\n",
      "try to drop unit 911 from 142 th HD.  usage down to 1.0348659369179185\n",
      "try to drop unit 911 from 213 th HD.  usage down to 1.0316516670801015\n",
      "try to drop unit 911 from 284 th HD.  usage down to 1.0283573119718319\n",
      "try to drop unit 911 from 355 th HD.  usage down to 1.0277641380194982\n",
      "all done trying to reduce usage of unit 911 final usage = 1.02776 754 sec elapsed 14 2 successful, failed patches, couldn't start= 341\n",
      "current avg and SD of usage are 1.00179 0.0441\n",
      "starting to reduce usage for unit 732 with usage 1.04055 . Total units tried = 20\n",
      "try to drop unit 732 from 30 th HD.  usage down to 1.0262574490330232\n",
      "all done trying to reduce usage of unit 732 final usage = 1.01956 777 sec elapsed 27 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00178 0.04387\n",
      "starting to reduce usage for unit 907 with usage 1.03808 . Total units tried = 21\n",
      "all done trying to reduce usage of unit 907 final usage = 1.01871 785 sec elapsed 15 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04368\n",
      "starting to reduce usage for unit 235 with usage 1.03804 . Total units tried = 22\n",
      "all done trying to reduce usage of unit 235 final usage = 1.0178 794 sec elapsed 22 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00178 0.04345\n",
      "starting to reduce usage for unit 2470 with usage 1.03595 . Total units tried = 23\n",
      "all done trying to reduce usage of unit 2470 final usage = 1.01828 798 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04336\n",
      "starting to reduce usage for unit 2010 with usage 1.03578 . Total units tried = 24\n",
      "all done trying to reduce usage of unit 2010 final usage = 1.01979 806 sec elapsed 9 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00178 0.04329\n",
      "starting to reduce usage for unit 220 with usage 1.03511 . Total units tried = 25\n",
      "all done trying to reduce usage of unit 220 final usage = 1.01972 817 sec elapsed 21 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00178 0.04311\n",
      "starting to reduce usage for unit 489 with usage 1.03462 . Total units tried = 26\n",
      "all done trying to reduce usage of unit 489 final usage = 1.01987 835 sec elapsed 16 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00178 0.04301\n",
      "starting to reduce usage for unit 376 with usage 1.03401 . Total units tried = 27\n",
      "all done trying to reduce usage of unit 376 final usage = 1.01683 844 sec elapsed 6 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00178 0.04285\n",
      "starting to reduce usage for unit 724 with usage 1.03269 . Total units tried = 28\n",
      "all done trying to reduce usage of unit 724 final usage = 1.01935 883 sec elapsed 19 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00178 0.0427\n",
      "starting to reduce usage for unit 6 with usage 1.03219 . Total units tried = 29\n",
      "all done trying to reduce usage of unit 6 final usage = 1.01897 893 sec elapsed 9 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00178 0.04261\n",
      "starting to reduce usage for unit 2295 with usage 1.0311 . Total units tried = 30\n",
      "all done trying to reduce usage of unit 2295 final usage = 1.01836 901 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00178 0.04256\n",
      "starting to reduce usage for unit 693 with usage 1.03017 . Total units tried = 31\n",
      "all done trying to reduce usage of unit 693 final usage = 1.01945 911 sec elapsed 15 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00178 0.04243\n",
      "starting to reduce usage for unit 2298 with usage 1.02864 . Total units tried = 32\n",
      "all done trying to reduce usage of unit 2298 final usage = 1.01937 920 sec elapsed 5 3 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00178 0.04238\n",
      "starting to reduce usage for unit 851 with usage 1.02854 . Total units tried = 33\n",
      "all done trying to reduce usage of unit 851 final usage = 1.01811 927 sec elapsed 12 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00178 0.04227\n",
      "starting to reduce usage for unit 297 with usage 1.02836 . Total units tried = 34\n",
      "all done trying to reduce usage of unit 297 final usage = 1.01791 935 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00178 0.04222\n",
      "starting to reduce usage for unit 9 with usage 1.02768 . Total units tried = 35\n",
      "all done trying to reduce usage of unit 9 final usage = 1.01843 939 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00178 0.04214\n",
      "starting to reduce usage for unit 227 with usage 1.02757 . Total units tried = 36\n",
      "try to drop unit 227 from 48 th HD.  usage down to 1.0208428703239625\n",
      "all done trying to reduce usage of unit 227 final usage = 1.01871 955 sec elapsed 14 0 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.00178 0.04203\n",
      "starting to reduce usage for unit 869 with usage 1.02704 . Total units tried = 37\n",
      "all done trying to reduce usage of unit 869 final usage = 1.01853 961 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04197\n",
      "starting to reduce usage for unit 516 with usage 1.03213 . Total units tried = 38\n",
      "all done trying to reduce usage of unit 516 final usage = 1.01997 977 sec elapsed 12 0 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00178 0.04188\n",
      "starting to reduce usage for unit 861 with usage 1.02587 . Total units tried = 39\n",
      "all done trying to reduce usage of unit 861 final usage = 1.01614 983 sec elapsed 4 1 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00178 0.04182\n",
      "starting to reduce usage for unit 956 with usage 1.02475 . Total units tried = 40\n",
      "all done trying to reduce usage of unit 956 final usage = 1.01932 992 sec elapsed 10 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00178 0.04176\n",
      "starting to reduce usage for unit 973 with usage 1.02455 . Total units tried = 41\n",
      "all done trying to reduce usage of unit 973 final usage = 1.01933 1002 sec elapsed 5 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00178 0.04171\n",
      "starting to reduce usage for unit 262 with usage 1.02408 . Total units tried = 42\n",
      "all done trying to reduce usage of unit 262 final usage = 1.02 1005 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04165\n",
      "starting to reduce usage for unit 2965 with usage 1.02408 . Total units tried = 43\n",
      "all done trying to reduce usage of unit 2965 final usage = 1.0195 1011 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04163\n",
      "starting to reduce usage for unit 2187 with usage 1.02992 . Total units tried = 44\n",
      "try to drop unit 2187 from 51 th HD.  usage down to 1.0292301056820163\n",
      "all done trying to reduce usage of unit 2187 final usage = 1.01773 1024 sec elapsed 7 5 successful, failed patches, couldn't start= 87\n",
      "current avg and SD of usage are 1.00178 0.04159\n",
      "starting to reduce usage for unit 523 with usage 1.02563 . Total units tried = 45\n",
      "all done trying to reduce usage of unit 523 final usage = 1.01866 1029 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04156\n",
      "starting to reduce usage for unit 2942 with usage 1.02303 . Total units tried = 46\n",
      "all done trying to reduce usage of unit 2942 final usage = 1.01945 1033 sec elapsed 4 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00178 0.04151\n",
      "starting to reduce usage for unit 930 with usage 1.0229 . Total units tried = 47\n",
      "all done trying to reduce usage of unit 930 final usage = 1.01984 1037 sec elapsed 2 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00177 0.04147\n",
      "starting to reduce usage for unit 925 with usage 1.02246 . Total units tried = 48\n",
      "all done trying to reduce usage of unit 925 final usage = 1.01933 1041 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04143\n",
      "starting to reduce usage for unit 2963 with usage 1.02182 . Total units tried = 49\n",
      "all done trying to reduce usage of unit 2963 final usage = 1.01996 1043 sec elapsed 2 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00177 0.04141\n",
      "starting to reduce usage for unit 287 with usage 1.02146 . Total units tried = 50\n",
      "all done trying to reduce usage of unit 287 final usage = 1.01995 1049 sec elapsed 2 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00177 0.0414\n",
      "starting to reduce usage for unit 2961 with usage 1.02138 . Total units tried = 51\n",
      "all done trying to reduce usage of unit 2961 final usage = 1.01978 1052 sec elapsed 2 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00177 0.04137\n",
      "starting to reduce usage for unit 655 with usage 1.02378 . Total units tried = 52\n",
      "all done trying to reduce usage of unit 655 final usage = 1.01732 1061 sec elapsed 6 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00177 0.04131\n",
      "starting to reduce usage for unit 2964 with usage 1.02097 . Total units tried = 53\n",
      "all done trying to reduce usage of unit 2964 final usage = 1.01972 1064 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.0413\n",
      "starting to reduce usage for unit 1721 with usage 1.02062 . Total units tried = 54\n",
      "all done trying to reduce usage of unit 1721 final usage = 1.01939 1068 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04129\n",
      "starting to reduce usage for unit 280 with usage 1.02125 . Total units tried = 55\n",
      "all done trying to reduce usage of unit 280 final usage = 1.01967 1071 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04128\n",
      "starting to reduce usage for unit 239 with usage 1.02039 . Total units tried = 56\n",
      "all done trying to reduce usage of unit 239 final usage = 1.0192 1074 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00177 0.04127\n",
      "starting to reduce usage for unit 1187 with usage 1.02015 . Total units tried = 57\n",
      "all done trying to reduce usage of unit 1187 final usage = 1.01818 1075 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04126\n",
      "starting to reduce usage for unit 2967 with usage 1.02011 . Total units tried = 58\n",
      "all done trying to reduce usage of unit 2967 final usage = 1.01902 1078 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00177 0.04125\n",
      "starting to reduce usage for unit 710 with usage 1.01999 . Total units tried = 59\n",
      "all done trying to reduce usage of unit 710 final usage = 1.01999 1081 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04125\n",
      "starting to reduce usage for unit 2419 with usage 1.01979 . Total units tried = 60\n",
      "all done trying to reduce usage of unit 2419 final usage = 1.01979 1083 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04125\n",
      "starting to reduce usage for unit 2002 with usage 1.01967 . Total units tried = 61\n",
      "all done trying to reduce usage of unit 2002 final usage = 1.01967 1086 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04125\n",
      "starting to reduce usage for unit 1999 with usage 1.01967 . Total units tried = 62\n",
      "all done trying to reduce usage of unit 1999 final usage = 1.01967 1089 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04125\n",
      "starting to reduce usage for unit 2418 with usage 1.02204 . Total units tried = 63\n",
      "all done trying to reduce usage of unit 2418 final usage = 1.01901 1095 sec elapsed 3 0 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 705 with usage 1.01929 . Total units tried = 64\n",
      "all done trying to reduce usage of unit 705 final usage = 1.01929 1098 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 959 with usage 1.01922 . Total units tried = 65\n",
      "all done trying to reduce usage of unit 959 final usage = 1.01922 1100 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 356 with usage 1.0192 . Total units tried = 66\n",
      "all done trying to reduce usage of unit 356 final usage = 1.0192 1102 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 692 with usage 1.01913 . Total units tried = 67\n",
      "all done trying to reduce usage of unit 692 final usage = 1.01913 1104 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 714 with usage 1.01911 . Total units tried = 68\n",
      "all done trying to reduce usage of unit 714 final usage = 1.01911 1107 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 691 with usage 1.01908 . Total units tried = 69\n",
      "all done trying to reduce usage of unit 691 final usage = 1.01908 1109 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2931 with usage 1.01898 . Total units tried = 70\n",
      "all done trying to reduce usage of unit 2931 final usage = 1.01898 1111 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 970 with usage 1.01894 . Total units tried = 71\n",
      "all done trying to reduce usage of unit 970 final usage = 1.01894 1114 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 221 with usage 1.01893 . Total units tried = 72\n",
      "all done trying to reduce usage of unit 221 final usage = 1.01893 1116 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 955 with usage 1.01888 . Total units tried = 73\n",
      "all done trying to reduce usage of unit 955 final usage = 1.01888 1119 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 222 with usage 1.01879 . Total units tried = 74\n",
      "all done trying to reduce usage of unit 222 final usage = 1.01879 1121 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 218 with usage 1.01877 . Total units tried = 75\n",
      "all done trying to reduce usage of unit 218 final usage = 1.01877 1124 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 700 with usage 1.01876 . Total units tried = 76\n",
      "all done trying to reduce usage of unit 700 final usage = 1.01876 1126 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 728 with usage 1.01873 . Total units tried = 77\n",
      "all done trying to reduce usage of unit 728 final usage = 1.01873 1129 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 219 with usage 1.01871 . Total units tried = 78\n",
      "all done trying to reduce usage of unit 219 final usage = 1.01871 1132 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 731 with usage 1.01868 . Total units tried = 79\n",
      "all done trying to reduce usage of unit 731 final usage = 1.01868 1134 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 719 with usage 1.01866 . Total units tried = 80\n",
      "all done trying to reduce usage of unit 719 final usage = 1.01866 1137 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 564 with usage 1.0192 . Total units tried = 81\n",
      "all done trying to reduce usage of unit 564 final usage = 1.0192 1140 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 733 with usage 1.01861 . Total units tried = 82\n",
      "all done trying to reduce usage of unit 733 final usage = 1.01861 1142 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 713 with usage 1.0186 . Total units tried = 83\n",
      "all done trying to reduce usage of unit 713 final usage = 1.0186 1145 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2407 with usage 1.01862 . Total units tried = 84\n",
      "all done trying to reduce usage of unit 2407 final usage = 1.01862 1148 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 910 with usage 1.01844 . Total units tried = 85\n",
      "all done trying to reduce usage of unit 910 final usage = 1.01844 1150 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1728 with usage 1.01844 . Total units tried = 86\n",
      "all done trying to reduce usage of unit 1728 final usage = 1.01844 1153 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 238 with usage 1.01842 . Total units tried = 87\n",
      "all done trying to reduce usage of unit 238 final usage = 1.01842 1155 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 726 with usage 1.01841 . Total units tried = 88\n",
      "all done trying to reduce usage of unit 726 final usage = 1.01841 1157 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 217 with usage 1.0184 . Total units tried = 89\n",
      "all done trying to reduce usage of unit 217 final usage = 1.0184 1160 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 741 with usage 1.01838 . Total units tried = 90\n",
      "all done trying to reduce usage of unit 741 final usage = 1.01838 1162 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 314 with usage 1.01837 . Total units tried = 91\n",
      "all done trying to reduce usage of unit 314 final usage = 1.01837 1164 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 740 with usage 1.01845 . Total units tried = 92\n",
      "all done trying to reduce usage of unit 740 final usage = 1.01845 1166 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 701 with usage 1.01832 . Total units tried = 93\n",
      "all done trying to reduce usage of unit 701 final usage = 1.01832 1169 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 704 with usage 1.01826 . Total units tried = 94\n",
      "all done trying to reduce usage of unit 704 final usage = 1.01826 1171 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 739 with usage 1.01824 . Total units tried = 95\n",
      "all done trying to reduce usage of unit 739 final usage = 1.01824 1173 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 241 with usage 1.01821 . Total units tried = 96\n",
      "all done trying to reduce usage of unit 241 final usage = 1.01821 1176 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 722 with usage 1.01815 . Total units tried = 97\n",
      "all done trying to reduce usage of unit 722 final usage = 1.01815 1178 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2968 with usage 1.01813 . Total units tried = 98\n",
      "all done trying to reduce usage of unit 2968 final usage = 1.01813 1181 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 246 with usage 1.0181 . Total units tried = 99\n",
      "all done trying to reduce usage of unit 246 final usage = 1.0181 1183 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1512 with usage 1.0181 . Total units tried = 100\n",
      "all done trying to reduce usage of unit 1512 final usage = 1.0181 1185 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 240 with usage 1.01804 . Total units tried = 101\n",
      "all done trying to reduce usage of unit 240 final usage = 1.01804 1187 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 694 with usage 1.018 . Total units tried = 102\n",
      "all done trying to reduce usage of unit 694 final usage = 1.018 1189 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 702 with usage 1.01796 . Total units tried = 103\n",
      "all done trying to reduce usage of unit 702 final usage = 1.01796 1191 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 716 with usage 1.0179 . Total units tried = 104\n",
      "all done trying to reduce usage of unit 716 final usage = 1.0179 1193 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 968 with usage 1.01787 . Total units tried = 105\n",
      "all done trying to reduce usage of unit 968 final usage = 1.01787 1195 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 737 with usage 1.01784 . Total units tried = 106\n",
      "all done trying to reduce usage of unit 737 final usage = 1.01784 1197 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 709 with usage 1.0178 . Total units tried = 107\n",
      "all done trying to reduce usage of unit 709 final usage = 1.0178 1199 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 736 with usage 1.01774 . Total units tried = 108\n",
      "all done trying to reduce usage of unit 736 final usage = 1.01774 1202 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 699 with usage 1.0177 . Total units tried = 109\n",
      "all done trying to reduce usage of unit 699 final usage = 1.0177 1204 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2946 with usage 1.01769 . Total units tried = 110\n",
      "all done trying to reduce usage of unit 2946 final usage = 1.01769 1206 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 742 with usage 1.01769 . Total units tried = 111\n",
      "all done trying to reduce usage of unit 742 final usage = 1.01769 1209 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 729 with usage 1.01769 . Total units tried = 112\n",
      "all done trying to reduce usage of unit 729 final usage = 1.01769 1211 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 715 with usage 1.01766 . Total units tried = 113\n",
      "all done trying to reduce usage of unit 715 final usage = 1.01766 1213 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2342 with usage 1.01766 . Total units tried = 114\n",
      "all done trying to reduce usage of unit 2342 final usage = 1.01766 1215 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2371 with usage 1.01766 . Total units tried = 115\n",
      "all done trying to reduce usage of unit 2371 final usage = 1.01766 1217 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2937 with usage 1.01764 . Total units tried = 116\n",
      "all done trying to reduce usage of unit 2937 final usage = 1.01764 1220 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 696 with usage 1.01764 . Total units tried = 117\n",
      "all done trying to reduce usage of unit 696 final usage = 1.01764 1222 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 837 with usage 1.01761 . Total units tried = 118\n",
      "all done trying to reduce usage of unit 837 final usage = 1.01761 1225 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 734 with usage 1.01758 . Total units tried = 119\n",
      "all done trying to reduce usage of unit 734 final usage = 1.01758 1227 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 242 with usage 1.01757 . Total units tried = 120\n",
      "all done trying to reduce usage of unit 242 final usage = 1.01757 1229 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 695 with usage 1.01752 . Total units tried = 121\n",
      "all done trying to reduce usage of unit 695 final usage = 1.01752 1231 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 720 with usage 1.01752 . Total units tried = 122\n",
      "all done trying to reduce usage of unit 720 final usage = 1.01752 1234 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 277 with usage 1.0175 . Total units tried = 123\n",
      "all done trying to reduce usage of unit 277 final usage = 1.0175 1236 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 245 with usage 1.01749 . Total units tried = 124\n",
      "all done trying to reduce usage of unit 245 final usage = 1.01749 1238 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 697 with usage 1.01749 . Total units tried = 125\n",
      "all done trying to reduce usage of unit 697 final usage = 1.01749 1240 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2559 with usage 1.01748 . Total units tried = 126\n",
      "all done trying to reduce usage of unit 2559 final usage = 1.01748 1243 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1716 with usage 1.01746 . Total units tried = 127\n",
      "all done trying to reduce usage of unit 1716 final usage = 1.01746 1245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 711 with usage 1.01743 . Total units tried = 128\n",
      "all done trying to reduce usage of unit 711 final usage = 1.01743 1248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 912 with usage 1.01743 . Total units tried = 129\n",
      "all done trying to reduce usage of unit 912 final usage = 1.01743 1251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 735 with usage 1.0174 . Total units tried = 130\n",
      "all done trying to reduce usage of unit 735 final usage = 1.0174 1253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 230 with usage 1.01736 . Total units tried = 131\n",
      "all done trying to reduce usage of unit 230 final usage = 1.01736 1255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 743 with usage 1.01736 . Total units tried = 132\n",
      "all done trying to reduce usage of unit 743 final usage = 1.01736 1258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 718 with usage 1.01733 . Total units tried = 133\n",
      "all done trying to reduce usage of unit 718 final usage = 1.01733 1260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2936 with usage 1.01731 . Total units tried = 134\n",
      "all done trying to reduce usage of unit 2936 final usage = 1.01731 1263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1734 with usage 1.01729 . Total units tried = 135\n",
      "all done trying to reduce usage of unit 1734 final usage = 1.01729 1266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 225 with usage 1.01726 . Total units tried = 136\n",
      "all done trying to reduce usage of unit 225 final usage = 1.01726 1268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 264 with usage 1.01723 . Total units tried = 137\n",
      "all done trying to reduce usage of unit 264 final usage = 1.01723 1271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 706 with usage 1.01723 . Total units tried = 138\n",
      "all done trying to reduce usage of unit 706 final usage = 1.01723 1273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 224 with usage 1.0172 . Total units tried = 139\n",
      "all done trying to reduce usage of unit 224 final usage = 1.0172 1275 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 725 with usage 1.01719 . Total units tried = 140\n",
      "all done trying to reduce usage of unit 725 final usage = 1.01719 1277 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 248 with usage 1.01719 . Total units tried = 141\n",
      "all done trying to reduce usage of unit 248 final usage = 1.01719 1280 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 298 with usage 1.01718 . Total units tried = 142\n",
      "all done trying to reduce usage of unit 298 final usage = 1.01718 1282 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 689 with usage 1.01717 . Total units tried = 143\n",
      "all done trying to reduce usage of unit 689 final usage = 1.01717 1284 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 707 with usage 1.01715 . Total units tried = 144\n",
      "all done trying to reduce usage of unit 707 final usage = 1.01715 1287 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 708 with usage 1.01714 . Total units tried = 145\n",
      "all done trying to reduce usage of unit 708 final usage = 1.01714 1289 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 244 with usage 1.01709 . Total units tried = 146\n",
      "all done trying to reduce usage of unit 244 final usage = 1.01709 1291 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 717 with usage 1.01707 . Total units tried = 147\n",
      "all done trying to reduce usage of unit 717 final usage = 1.01707 1293 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 645 with usage 1.01707 . Total units tried = 148\n",
      "all done trying to reduce usage of unit 645 final usage = 1.01707 1296 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 712 with usage 1.01704 . Total units tried = 149\n",
      "all done trying to reduce usage of unit 712 final usage = 1.01704 1298 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1722 with usage 1.01703 . Total units tried = 150\n",
      "all done trying to reduce usage of unit 1722 final usage = 1.01703 1301 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 727 with usage 1.01702 . Total units tried = 151\n",
      "all done trying to reduce usage of unit 727 final usage = 1.01702 1303 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 237 with usage 1.01702 . Total units tried = 152\n",
      "all done trying to reduce usage of unit 237 final usage = 1.01702 1305 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1723 with usage 1.01701 . Total units tried = 153\n",
      "all done trying to reduce usage of unit 1723 final usage = 1.01701 1308 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 977 with usage 1.01695 . Total units tried = 154\n",
      "all done trying to reduce usage of unit 977 final usage = 1.01695 1310 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 642 with usage 1.01695 . Total units tried = 155\n",
      "all done trying to reduce usage of unit 642 final usage = 1.01695 1313 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2529 with usage 1.01694 . Total units tried = 156\n",
      "all done trying to reduce usage of unit 2529 final usage = 1.01694 1316 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1711 with usage 1.01691 . Total units tried = 157\n",
      "all done trying to reduce usage of unit 1711 final usage = 1.01691 1319 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1724 with usage 1.01689 . Total units tried = 158\n",
      "all done trying to reduce usage of unit 1724 final usage = 1.01689 1322 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 652 with usage 1.01688 . Total units tried = 159\n",
      "all done trying to reduce usage of unit 652 final usage = 1.01688 1325 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 721 with usage 1.01688 . Total units tried = 160\n",
      "all done trying to reduce usage of unit 721 final usage = 1.01688 1327 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 703 with usage 1.01683 . Total units tried = 161\n",
      "all done trying to reduce usage of unit 703 final usage = 1.01683 1329 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2933 with usage 1.01682 . Total units tried = 162\n",
      "all done trying to reduce usage of unit 2933 final usage = 1.01682 1332 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2955 with usage 1.01681 . Total units tried = 163\n",
      "all done trying to reduce usage of unit 2955 final usage = 1.01681 1335 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 891 with usage 1.0168 . Total units tried = 164\n",
      "all done trying to reduce usage of unit 891 final usage = 1.0168 1337 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 226 with usage 1.01679 . Total units tried = 165\n",
      "all done trying to reduce usage of unit 226 final usage = 1.01679 1339 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 723 with usage 1.01677 . Total units tried = 166\n",
      "all done trying to reduce usage of unit 723 final usage = 1.01677 1341 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 520 with usage 1.01677 . Total units tried = 167\n",
      "all done trying to reduce usage of unit 520 final usage = 1.01677 1343 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2416 with usage 1.01674 . Total units tried = 168\n",
      "all done trying to reduce usage of unit 2416 final usage = 1.01674 1345 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2500 with usage 1.01673 . Total units tried = 169\n",
      "all done trying to reduce usage of unit 2500 final usage = 1.01673 1348 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2278 with usage 1.01671 . Total units tried = 170\n",
      "all done trying to reduce usage of unit 2278 final usage = 1.01671 1350 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 229 with usage 1.01665 . Total units tried = 171\n",
      "all done trying to reduce usage of unit 229 final usage = 1.01665 1352 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 228 with usage 1.01665 . Total units tried = 172\n",
      "all done trying to reduce usage of unit 228 final usage = 1.01665 1354 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 236 with usage 1.01665 . Total units tried = 173\n",
      "all done trying to reduce usage of unit 236 final usage = 1.01665 1357 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 957 with usage 1.01665 . Total units tried = 174\n",
      "all done trying to reduce usage of unit 957 final usage = 1.01665 1359 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 641 with usage 1.01663 . Total units tried = 175\n",
      "all done trying to reduce usage of unit 641 final usage = 1.01663 1362 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 243 with usage 1.0166 . Total units tried = 176\n",
      "all done trying to reduce usage of unit 243 final usage = 1.0166 1364 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 370 with usage 1.01659 . Total units tried = 177\n",
      "all done trying to reduce usage of unit 370 final usage = 1.01659 1366 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2935 with usage 1.01656 . Total units tried = 178\n",
      "all done trying to reduce usage of unit 2935 final usage = 1.01656 1369 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 834 with usage 1.01655 . Total units tried = 179\n",
      "all done trying to reduce usage of unit 834 final usage = 1.01655 1371 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 969 with usage 1.01649 . Total units tried = 180\n",
      "all done trying to reduce usage of unit 969 final usage = 1.01649 1374 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2502 with usage 1.01641 . Total units tried = 181\n",
      "all done trying to reduce usage of unit 2502 final usage = 1.01641 1377 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 646 with usage 1.0164 . Total units tried = 182\n",
      "all done trying to reduce usage of unit 646 final usage = 1.0164 1379 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2154 with usage 1.0164 . Total units tried = 183\n",
      "all done trying to reduce usage of unit 2154 final usage = 1.0164 1381 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1719 with usage 1.01638 . Total units tried = 184\n",
      "all done trying to reduce usage of unit 1719 final usage = 1.01638 1384 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2540 with usage 1.01637 . Total units tried = 185\n",
      "all done trying to reduce usage of unit 2540 final usage = 1.01637 1386 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 616 with usage 1.01637 . Total units tried = 186\n",
      "all done trying to reduce usage of unit 616 final usage = 1.01637 1389 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 495 with usage 1.01636 . Total units tried = 187\n",
      "all done trying to reduce usage of unit 495 final usage = 1.01636 1391 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 253 with usage 1.01632 . Total units tried = 188\n",
      "all done trying to reduce usage of unit 253 final usage = 1.01632 1394 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2958 with usage 1.0163 . Total units tried = 189\n",
      "all done trying to reduce usage of unit 2958 final usage = 1.0163 1397 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 234 with usage 1.01629 . Total units tried = 190\n",
      "all done trying to reduce usage of unit 234 final usage = 1.01629 1399 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 758 with usage 1.01625 . Total units tried = 191\n",
      "all done trying to reduce usage of unit 758 final usage = 1.01625 1402 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1138 with usage 1.01624 . Total units tried = 192\n",
      "all done trying to reduce usage of unit 1138 final usage = 1.01624 1405 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1731 with usage 1.01623 . Total units tried = 193\n",
      "all done trying to reduce usage of unit 1731 final usage = 1.01623 1408 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 802 with usage 1.01613 . Total units tried = 194\n",
      "all done trying to reduce usage of unit 802 final usage = 1.01613 1410 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 762 with usage 1.01608 . Total units tried = 195\n",
      "all done trying to reduce usage of unit 762 final usage = 1.01608 1413 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1732 with usage 1.01608 . Total units tried = 196\n",
      "all done trying to reduce usage of unit 1732 final usage = 1.01608 1416 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2941 with usage 1.01606 . Total units tried = 197\n",
      "all done trying to reduce usage of unit 2941 final usage = 1.01606 1419 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2401 with usage 1.01604 . Total units tried = 198\n",
      "all done trying to reduce usage of unit 2401 final usage = 1.01604 1421 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 12 with usage 1.016 . Total units tried = 199\n",
      "all done trying to reduce usage of unit 12 final usage = 1.016 1424 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 810 with usage 1.01594 . Total units tried = 200\n",
      "all done trying to reduce usage of unit 810 final usage = 1.01594 1426 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 13 with usage 1.01589 . Total units tried = 201\n",
      "all done trying to reduce usage of unit 13 final usage = 1.01589 1430 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1717 with usage 1.01587 . Total units tried = 202\n",
      "all done trying to reduce usage of unit 1717 final usage = 1.01587 1433 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2430 with usage 1.01583 . Total units tried = 203\n",
      "all done trying to reduce usage of unit 2430 final usage = 1.01583 1436 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 216 with usage 1.0158 . Total units tried = 204\n",
      "all done trying to reduce usage of unit 216 final usage = 1.0158 1439 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1713 with usage 1.01574 . Total units tried = 205\n",
      "all done trying to reduce usage of unit 1713 final usage = 1.01574 1442 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 975 with usage 1.01569 . Total units tried = 206\n",
      "all done trying to reduce usage of unit 975 final usage = 1.01569 1446 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 281 with usage 1.01555 . Total units tried = 207\n",
      "all done trying to reduce usage of unit 281 final usage = 1.01555 1449 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2431 with usage 1.0154 . Total units tried = 208\n",
      "all done trying to reduce usage of unit 2431 final usage = 1.0154 1452 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 767 with usage 1.01537 . Total units tried = 209\n",
      "all done trying to reduce usage of unit 767 final usage = 1.01537 1454 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 938 with usage 1.01535 . Total units tried = 210\n",
      "all done trying to reduce usage of unit 938 final usage = 1.01535 1456 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 902 with usage 1.01532 . Total units tried = 211\n",
      "all done trying to reduce usage of unit 902 final usage = 1.01532 1459 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2403 with usage 1.01532 . Total units tried = 212\n",
      "all done trying to reduce usage of unit 2403 final usage = 1.01532 1461 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 916 with usage 1.01526 . Total units tried = 213\n",
      "all done trying to reduce usage of unit 916 final usage = 1.01526 1464 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 764 with usage 1.01524 . Total units tried = 214\n",
      "all done trying to reduce usage of unit 764 final usage = 1.01524 1467 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 301 with usage 1.01522 . Total units tried = 215\n",
      "all done trying to reduce usage of unit 301 final usage = 1.01522 1470 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1710 with usage 1.01522 . Total units tried = 216\n",
      "all done trying to reduce usage of unit 1710 final usage = 1.01522 1472 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 632 with usage 1.0152 . Total units tried = 217\n",
      "all done trying to reduce usage of unit 632 final usage = 1.0152 1475 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 4 with usage 1.01515 . Total units tried = 218\n",
      "all done trying to reduce usage of unit 4 final usage = 1.01515 1478 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 768 with usage 1.01511 . Total units tried = 219\n",
      "all done trying to reduce usage of unit 768 final usage = 1.01511 1481 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 978 with usage 1.01504 . Total units tried = 220\n",
      "all done trying to reduce usage of unit 978 final usage = 1.01504 1483 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2943 with usage 1.01497 . Total units tried = 221\n",
      "all done trying to reduce usage of unit 2943 final usage = 1.01497 1486 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1131 with usage 1.01495 . Total units tried = 222\n",
      "all done trying to reduce usage of unit 1131 final usage = 1.01495 1488 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 263 with usage 1.01495 . Total units tried = 223\n",
      "all done trying to reduce usage of unit 263 final usage = 1.01495 1491 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 276 with usage 1.01494 . Total units tried = 224\n",
      "all done trying to reduce usage of unit 276 final usage = 1.01494 1494 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 503 with usage 1.01492 . Total units tried = 225\n",
      "all done trying to reduce usage of unit 503 final usage = 1.01492 1496 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2940 with usage 1.01489 . Total units tried = 226\n",
      "all done trying to reduce usage of unit 2940 final usage = 1.01489 1499 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2405 with usage 1.01489 . Total units tried = 227\n",
      "all done trying to reduce usage of unit 2405 final usage = 1.01489 1501 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 844 with usage 1.01488 . Total units tried = 228\n",
      "all done trying to reduce usage of unit 844 final usage = 1.01488 1504 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2131 with usage 1.01485 . Total units tried = 229\n",
      "all done trying to reduce usage of unit 2131 final usage = 1.01485 1506 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 249 with usage 1.01484 . Total units tried = 230\n",
      "all done trying to reduce usage of unit 249 final usage = 1.01484 1509 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 651 with usage 1.01483 . Total units tried = 231\n",
      "all done trying to reduce usage of unit 651 final usage = 1.01483 1512 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2512 with usage 1.01477 . Total units tried = 232\n",
      "all done trying to reduce usage of unit 2512 final usage = 1.01477 1514 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1720 with usage 1.01477 . Total units tried = 233\n",
      "all done trying to reduce usage of unit 1720 final usage = 1.01477 1517 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 909 with usage 1.01475 . Total units tried = 234\n",
      "all done trying to reduce usage of unit 909 final usage = 1.01475 1520 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1714 with usage 1.01474 . Total units tried = 235\n",
      "all done trying to reduce usage of unit 1714 final usage = 1.01474 1522 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2127 with usage 1.01474 . Total units tried = 236\n",
      "all done trying to reduce usage of unit 2127 final usage = 1.01474 1525 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2949 with usage 1.01473 . Total units tried = 237\n",
      "all done trying to reduce usage of unit 2949 final usage = 1.01473 1528 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 258 with usage 1.01471 . Total units tried = 238\n",
      "all done trying to reduce usage of unit 258 final usage = 1.01471 1530 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2132 with usage 1.01471 . Total units tried = 239\n",
      "all done trying to reduce usage of unit 2132 final usage = 1.01471 1533 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2433 with usage 1.01467 . Total units tried = 240\n",
      "all done trying to reduce usage of unit 2433 final usage = 1.01467 1535 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 623 with usage 1.01465 . Total units tried = 241\n",
      "all done trying to reduce usage of unit 623 final usage = 1.01465 1538 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2282 with usage 1.01464 . Total units tried = 242\n",
      "all done trying to reduce usage of unit 2282 final usage = 1.01464 1540 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2404 with usage 1.01463 . Total units tried = 243\n",
      "all done trying to reduce usage of unit 2404 final usage = 1.01463 1542 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 369 with usage 1.01455 . Total units tried = 244\n",
      "all done trying to reduce usage of unit 369 final usage = 1.01455 1544 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 828 with usage 1.01454 . Total units tried = 245\n",
      "all done trying to reduce usage of unit 828 final usage = 1.01454 1547 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2938 with usage 1.01453 . Total units tried = 246\n",
      "all done trying to reduce usage of unit 2938 final usage = 1.01453 1550 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 14 with usage 1.0145 . Total units tried = 247\n",
      "all done trying to reduce usage of unit 14 final usage = 1.0145 1553 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2969 with usage 1.0145 . Total units tried = 248\n",
      "all done trying to reduce usage of unit 2969 final usage = 1.0145 1556 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1715 with usage 1.01448 . Total units tried = 249\n",
      "all done trying to reduce usage of unit 1715 final usage = 1.01448 1559 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 618 with usage 1.01448 . Total units tried = 250\n",
      "all done trying to reduce usage of unit 618 final usage = 1.01448 1562 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2216 with usage 1.01447 . Total units tried = 251\n",
      "all done trying to reduce usage of unit 2216 final usage = 1.01447 1564 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2280 with usage 1.01446 . Total units tried = 252\n",
      "all done trying to reduce usage of unit 2280 final usage = 1.01446 1566 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 854 with usage 1.01443 . Total units tried = 253\n",
      "all done trying to reduce usage of unit 854 final usage = 1.01443 1569 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 648 with usage 1.01442 . Total units tried = 254\n",
      "all done trying to reduce usage of unit 648 final usage = 1.01442 1572 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 626 with usage 1.01442 . Total units tried = 255\n",
      "all done trying to reduce usage of unit 626 final usage = 1.01442 1575 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 847 with usage 1.01442 . Total units tried = 256\n",
      "all done trying to reduce usage of unit 847 final usage = 1.01442 1577 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 299 with usage 1.01435 . Total units tried = 257\n",
      "all done trying to reduce usage of unit 299 final usage = 1.01435 1579 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 257 with usage 1.01435 . Total units tried = 258\n",
      "all done trying to reduce usage of unit 257 final usage = 1.01435 1581 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2125 with usage 1.01434 . Total units tried = 259\n",
      "all done trying to reduce usage of unit 2125 final usage = 1.01434 1584 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2402 with usage 1.01432 . Total units tried = 260\n",
      "all done trying to reduce usage of unit 2402 final usage = 1.01432 1587 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 824 with usage 1.01431 . Total units tried = 261\n",
      "all done trying to reduce usage of unit 824 final usage = 1.01431 1589 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1730 with usage 1.01431 . Total units tried = 262\n",
      "all done trying to reduce usage of unit 1730 final usage = 1.01431 1592 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 839 with usage 1.0143 . Total units tried = 263\n",
      "all done trying to reduce usage of unit 839 final usage = 1.0143 1594 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 261 with usage 1.01429 . Total units tried = 264\n",
      "all done trying to reduce usage of unit 261 final usage = 1.01429 1597 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 849 with usage 1.01429 . Total units tried = 265\n",
      "all done trying to reduce usage of unit 849 final usage = 1.01429 1600 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 622 with usage 1.01428 . Total units tried = 266\n",
      "all done trying to reduce usage of unit 622 final usage = 1.01428 1603 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 250 with usage 1.01427 . Total units tried = 267\n",
      "all done trying to reduce usage of unit 250 final usage = 1.01427 1605 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 661 with usage 1.01425 . Total units tried = 268\n",
      "all done trying to reduce usage of unit 661 final usage = 1.01425 1608 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2930 with usage 1.01422 . Total units tried = 269\n",
      "all done trying to reduce usage of unit 2930 final usage = 1.01422 1611 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2945 with usage 1.01421 . Total units tried = 270\n",
      "all done trying to reduce usage of unit 2945 final usage = 1.01421 1614 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 647 with usage 1.01419 . Total units tried = 271\n",
      "all done trying to reduce usage of unit 647 final usage = 1.01419 1616 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 804 with usage 1.01416 . Total units tried = 272\n",
      "all done trying to reduce usage of unit 804 final usage = 1.01416 1618 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1726 with usage 1.01416 . Total units tried = 273\n",
      "all done trying to reduce usage of unit 1726 final usage = 1.01416 1621 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2413 with usage 1.01414 . Total units tried = 274\n",
      "all done trying to reduce usage of unit 2413 final usage = 1.01414 1624 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 290 with usage 1.01413 . Total units tried = 275\n",
      "all done trying to reduce usage of unit 290 final usage = 1.01413 1626 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 658 with usage 1.0141 . Total units tried = 276\n",
      "all done trying to reduce usage of unit 658 final usage = 1.0141 1629 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 265 with usage 1.0141 . Total units tried = 277\n",
      "all done trying to reduce usage of unit 265 final usage = 1.0141 1631 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1140 with usage 1.01407 . Total units tried = 278\n",
      "all done trying to reduce usage of unit 1140 final usage = 1.01407 1634 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 338 with usage 1.01405 . Total units tried = 279\n",
      "all done trying to reduce usage of unit 338 final usage = 1.01405 1636 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2301 with usage 1.01403 . Total units tried = 280\n",
      "all done trying to reduce usage of unit 2301 final usage = 1.01403 1639 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1727 with usage 1.01402 . Total units tried = 281\n",
      "all done trying to reduce usage of unit 1727 final usage = 1.01402 1642 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2400 with usage 1.01401 . Total units tried = 282\n",
      "all done trying to reduce usage of unit 2400 final usage = 1.01401 1645 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 918 with usage 1.01397 . Total units tried = 283\n",
      "all done trying to reduce usage of unit 918 final usage = 1.01397 1647 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 615 with usage 1.01394 . Total units tried = 284\n",
      "all done trying to reduce usage of unit 615 final usage = 1.01394 1650 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 832 with usage 1.01393 . Total units tried = 285\n",
      "all done trying to reduce usage of unit 832 final usage = 1.01393 1653 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2292 with usage 1.01386 . Total units tried = 286\n",
      "all done trying to reduce usage of unit 2292 final usage = 1.01386 1655 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 631 with usage 1.01385 . Total units tried = 287\n",
      "all done trying to reduce usage of unit 631 final usage = 1.01385 1658 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2959 with usage 1.01385 . Total units tried = 288\n",
      "all done trying to reduce usage of unit 2959 final usage = 1.01385 1661 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 746 with usage 1.01381 . Total units tried = 289\n",
      "all done trying to reduce usage of unit 746 final usage = 1.01381 1663 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 649 with usage 1.0138 . Total units tried = 290\n",
      "all done trying to reduce usage of unit 649 final usage = 1.0138 1666 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 830 with usage 1.0138 . Total units tried = 291\n",
      "all done trying to reduce usage of unit 830 final usage = 1.0138 1669 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1130 with usage 1.01379 . Total units tried = 292\n",
      "all done trying to reduce usage of unit 1130 final usage = 1.01379 1672 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 272 with usage 1.01379 . Total units tried = 293\n",
      "all done trying to reduce usage of unit 272 final usage = 1.01379 1674 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 915 with usage 1.01378 . Total units tried = 294\n",
      "all done trying to reduce usage of unit 915 final usage = 1.01378 1677 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1134 with usage 1.01377 . Total units tried = 295\n",
      "all done trying to reduce usage of unit 1134 final usage = 1.01377 1680 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 656 with usage 1.01376 . Total units tried = 296\n",
      "all done trying to reduce usage of unit 656 final usage = 1.01376 1683 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1718 with usage 1.01375 . Total units tried = 297\n",
      "all done trying to reduce usage of unit 1718 final usage = 1.01375 1685 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 288 with usage 1.01374 . Total units tried = 298\n",
      "all done trying to reduce usage of unit 288 final usage = 1.01374 1688 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 8 with usage 1.01374 . Total units tried = 299\n",
      "all done trying to reduce usage of unit 8 final usage = 1.01374 1691 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 829 with usage 1.01372 . Total units tried = 300\n",
      "all done trying to reduce usage of unit 829 final usage = 1.01372 1694 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2939 with usage 1.01371 . Total units tried = 301\n",
      "all done trying to reduce usage of unit 2939 final usage = 1.01371 1696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1733 with usage 1.0137 . Total units tried = 302\n",
      "all done trying to reduce usage of unit 1733 final usage = 1.0137 1699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 845 with usage 1.01369 . Total units tried = 303\n",
      "all done trying to reduce usage of unit 845 final usage = 1.01369 1702 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2397 with usage 1.01367 . Total units tried = 304\n",
      "all done trying to reduce usage of unit 2397 final usage = 1.01367 1704 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 821 with usage 1.01366 . Total units tried = 305\n",
      "all done trying to reduce usage of unit 821 final usage = 1.01366 1706 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2285 with usage 1.01365 . Total units tried = 306\n",
      "all done trying to reduce usage of unit 2285 final usage = 1.01365 1708 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 634 with usage 1.01364 . Total units tried = 307\n",
      "all done trying to reduce usage of unit 634 final usage = 1.01364 1711 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 848 with usage 1.01361 . Total units tried = 308\n",
      "all done trying to reduce usage of unit 848 final usage = 1.01361 1713 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2308 with usage 1.0136 . Total units tried = 309\n",
      "all done trying to reduce usage of unit 2308 final usage = 1.0136 1716 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 271 with usage 1.01359 . Total units tried = 310\n",
      "all done trying to reduce usage of unit 271 final usage = 1.01359 1719 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2296 with usage 1.01358 . Total units tried = 311\n",
      "all done trying to reduce usage of unit 2296 final usage = 1.01358 1721 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2424 with usage 1.01358 . Total units tried = 312\n",
      "all done trying to reduce usage of unit 2424 final usage = 1.01358 1724 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 730 with usage 1.01357 . Total units tried = 313\n",
      "all done trying to reduce usage of unit 730 final usage = 1.01357 1726 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 650 with usage 1.01355 . Total units tried = 314\n",
      "all done trying to reduce usage of unit 650 final usage = 1.01355 1728 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 852 with usage 1.01355 . Total units tried = 315\n",
      "all done trying to reduce usage of unit 852 final usage = 1.01355 1731 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 204 with usage 1.01355 . Total units tried = 316\n",
      "all done trying to reduce usage of unit 204 final usage = 1.01355 1733 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 440 with usage 1.01354 . Total units tried = 317\n",
      "all done trying to reduce usage of unit 440 final usage = 1.01354 1735 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1128 with usage 1.01353 . Total units tried = 318\n",
      "all done trying to reduce usage of unit 1128 final usage = 1.01353 1738 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2951 with usage 1.0135 . Total units tried = 319\n",
      "all done trying to reduce usage of unit 2951 final usage = 1.0135 1741 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 796 with usage 1.0135 . Total units tried = 320\n",
      "all done trying to reduce usage of unit 796 final usage = 1.0135 1744 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 203 with usage 1.0135 . Total units tried = 321\n",
      "all done trying to reduce usage of unit 203 final usage = 1.0135 1747 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 286 with usage 1.01348 . Total units tried = 322\n",
      "all done trying to reduce usage of unit 286 final usage = 1.01348 1750 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 853 with usage 1.01345 . Total units tried = 323\n",
      "all done trying to reduce usage of unit 853 final usage = 1.01345 1753 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 886 with usage 1.01344 . Total units tried = 324\n",
      "all done trying to reduce usage of unit 886 final usage = 1.01344 1755 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 451 with usage 1.01344 . Total units tried = 325\n",
      "all done trying to reduce usage of unit 451 final usage = 1.01344 1757 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2291 with usage 1.01343 . Total units tried = 326\n",
      "all done trying to reduce usage of unit 2291 final usage = 1.01343 1759 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 273 with usage 1.01343 . Total units tried = 327\n",
      "all done trying to reduce usage of unit 273 final usage = 1.01343 1762 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2287 with usage 1.0134 . Total units tried = 328\n",
      "all done trying to reduce usage of unit 2287 final usage = 1.0134 1764 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 251 with usage 1.01337 . Total units tried = 329\n",
      "all done trying to reduce usage of unit 251 final usage = 1.01337 1767 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 252 with usage 1.01337 . Total units tried = 330\n",
      "all done trying to reduce usage of unit 252 final usage = 1.01337 1769 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 795 with usage 1.01335 . Total units tried = 331\n",
      "all done trying to reduce usage of unit 795 final usage = 1.01335 1772 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2957 with usage 1.01335 . Total units tried = 332\n",
      "all done trying to reduce usage of unit 2957 final usage = 1.01335 1775 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 247 with usage 1.01335 . Total units tried = 333\n",
      "all done trying to reduce usage of unit 247 final usage = 1.01335 1777 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1729 with usage 1.01334 . Total units tried = 334\n",
      "all done trying to reduce usage of unit 1729 final usage = 1.01334 1780 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2954 with usage 1.01334 . Total units tried = 335\n",
      "all done trying to reduce usage of unit 2954 final usage = 1.01334 1782 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1186 with usage 1.01333 . Total units tried = 336\n",
      "all done trying to reduce usage of unit 1186 final usage = 1.01333 1784 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 415 with usage 1.01332 . Total units tried = 337\n",
      "all done trying to reduce usage of unit 415 final usage = 1.01332 1786 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 630 with usage 1.01331 . Total units tried = 338\n",
      "all done trying to reduce usage of unit 630 final usage = 1.01331 1789 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 846 with usage 1.01331 . Total units tried = 339\n",
      "all done trying to reduce usage of unit 846 final usage = 1.01331 1792 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2624 with usage 1.01331 . Total units tried = 340\n",
      "all done trying to reduce usage of unit 2624 final usage = 1.01331 1794 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 266 with usage 1.01328 . Total units tried = 341\n",
      "all done trying to reduce usage of unit 266 final usage = 1.01328 1796 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 296 with usage 1.01327 . Total units tried = 342\n",
      "all done trying to reduce usage of unit 296 final usage = 1.01327 1798 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1836 with usage 1.01326 . Total units tried = 343\n",
      "all done trying to reduce usage of unit 1836 final usage = 1.01326 1800 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 484 with usage 1.01326 . Total units tried = 344\n",
      "all done trying to reduce usage of unit 484 final usage = 1.01326 1803 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1856 with usage 1.01325 . Total units tried = 345\n",
      "all done trying to reduce usage of unit 1856 final usage = 1.01325 1805 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2947 with usage 1.01325 . Total units tried = 346\n",
      "all done trying to reduce usage of unit 2947 final usage = 1.01325 1807 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1791 with usage 1.01325 . Total units tried = 347\n",
      "all done trying to reduce usage of unit 1791 final usage = 1.01325 1809 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1403 with usage 1.01324 . Total units tried = 348\n",
      "all done trying to reduce usage of unit 1403 final usage = 1.01324 1811 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 485 with usage 1.01324 . Total units tried = 349\n",
      "all done trying to reduce usage of unit 485 final usage = 1.01324 1813 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2950 with usage 1.01322 . Total units tried = 350\n",
      "all done trying to reduce usage of unit 2950 final usage = 1.01322 1816 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2952 with usage 1.0132 . Total units tried = 351\n",
      "all done trying to reduce usage of unit 2952 final usage = 1.0132 1819 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1712 with usage 1.0132 . Total units tried = 352\n",
      "all done trying to reduce usage of unit 1712 final usage = 1.0132 1822 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 259 with usage 1.0132 . Total units tried = 353\n",
      "all done trying to reduce usage of unit 259 final usage = 1.0132 1824 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 882 with usage 1.01317 . Total units tried = 354\n",
      "all done trying to reduce usage of unit 882 final usage = 1.01317 1827 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 841 with usage 1.01314 . Total units tried = 355\n",
      "all done trying to reduce usage of unit 841 final usage = 1.01314 1830 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 744 with usage 1.01314 . Total units tried = 356\n",
      "all done trying to reduce usage of unit 744 final usage = 1.01314 1832 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1909 with usage 1.01312 . Total units tried = 357\n",
      "all done trying to reduce usage of unit 1909 final usage = 1.01312 1834 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 275 with usage 1.0131 . Total units tried = 358\n",
      "all done trying to reduce usage of unit 275 final usage = 1.0131 1837 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1364 with usage 1.0131 . Total units tried = 359\n",
      "all done trying to reduce usage of unit 1364 final usage = 1.0131 1838 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 521 with usage 1.01309 . Total units tried = 360\n",
      "all done trying to reduce usage of unit 521 final usage = 1.01309 1841 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 913 with usage 1.01308 . Total units tried = 361\n",
      "all done trying to reduce usage of unit 913 final usage = 1.01308 1844 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2157 with usage 1.01304 . Total units tried = 362\n",
      "all done trying to reduce usage of unit 2157 final usage = 1.01304 1845 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1136 with usage 1.01303 . Total units tried = 363\n",
      "all done trying to reduce usage of unit 1136 final usage = 1.01303 1848 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1832 with usage 1.01302 . Total units tried = 364\n",
      "all done trying to reduce usage of unit 1832 final usage = 1.01302 1850 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 638 with usage 1.013 . Total units tried = 365\n",
      "all done trying to reduce usage of unit 638 final usage = 1.013 1853 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 888 with usage 1.01299 . Total units tried = 366\n",
      "all done trying to reduce usage of unit 888 final usage = 1.01299 1855 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1879 with usage 1.01296 . Total units tried = 367\n",
      "all done trying to reduce usage of unit 1879 final usage = 1.01296 1857 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2306 with usage 1.01294 . Total units tried = 368\n",
      "all done trying to reduce usage of unit 2306 final usage = 1.01294 1860 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2189 with usage 1.01294 . Total units tried = 369\n",
      "all done trying to reduce usage of unit 2189 final usage = 1.01294 1862 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 827 with usage 1.01294 . Total units tried = 370\n",
      "all done trying to reduce usage of unit 827 final usage = 1.01294 1864 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 825 with usage 1.01293 . Total units tried = 371\n",
      "all done trying to reduce usage of unit 825 final usage = 1.01293 1867 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2293 with usage 1.01292 . Total units tried = 372\n",
      "all done trying to reduce usage of unit 2293 final usage = 1.01292 1869 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 877 with usage 1.01292 . Total units tried = 373\n",
      "all done trying to reduce usage of unit 877 final usage = 1.01292 1872 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 624 with usage 1.01292 . Total units tried = 374\n",
      "all done trying to reduce usage of unit 624 final usage = 1.01292 1874 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 643 with usage 1.0129 . Total units tried = 375\n",
      "all done trying to reduce usage of unit 643 final usage = 1.0129 1876 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2636 with usage 1.01289 . Total units tried = 376\n",
      "all done trying to reduce usage of unit 2636 final usage = 1.01289 1878 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 5 with usage 1.01289 . Total units tried = 377\n",
      "all done trying to reduce usage of unit 5 final usage = 1.01289 1881 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 826 with usage 1.01287 . Total units tried = 378\n",
      "all done trying to reduce usage of unit 826 final usage = 1.01287 1884 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 289 with usage 1.01287 . Total units tried = 379\n",
      "all done trying to reduce usage of unit 289 final usage = 1.01287 1886 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 881 with usage 1.01286 . Total units tried = 380\n",
      "all done trying to reduce usage of unit 881 final usage = 1.01286 1888 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2164 with usage 1.01283 . Total units tried = 381\n",
      "all done trying to reduce usage of unit 2164 final usage = 1.01283 1890 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1132 with usage 1.01281 . Total units tried = 382\n",
      "all done trying to reduce usage of unit 1132 final usage = 1.01281 1894 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 898 with usage 1.01281 . Total units tried = 383\n",
      "all done trying to reduce usage of unit 898 final usage = 1.01281 1896 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 819 with usage 1.0128 . Total units tried = 384\n",
      "all done trying to reduce usage of unit 819 final usage = 1.0128 1898 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 690 with usage 1.01279 . Total units tried = 385\n",
      "all done trying to reduce usage of unit 690 final usage = 1.01279 1900 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 976 with usage 1.01279 . Total units tried = 386\n",
      "all done trying to reduce usage of unit 976 final usage = 1.01279 1903 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 605 with usage 1.01277 . Total units tried = 387\n",
      "all done trying to reduce usage of unit 605 final usage = 1.01277 1905 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2932 with usage 1.01277 . Total units tried = 388\n",
      "all done trying to reduce usage of unit 2932 final usage = 1.01277 1907 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 256 with usage 1.01277 . Total units tried = 389\n",
      "all done trying to reduce usage of unit 256 final usage = 1.01277 1910 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 636 with usage 1.01275 . Total units tried = 390\n",
      "all done trying to reduce usage of unit 636 final usage = 1.01275 1912 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2483 with usage 1.01274 . Total units tried = 391\n",
      "all done trying to reduce usage of unit 2483 final usage = 1.01274 1914 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 874 with usage 1.01272 . Total units tried = 392\n",
      "all done trying to reduce usage of unit 874 final usage = 1.01272 1917 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 780 with usage 1.01272 . Total units tried = 393\n",
      "all done trying to reduce usage of unit 780 final usage = 1.01272 1919 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 10 with usage 1.01272 . Total units tried = 394\n",
      "all done trying to reduce usage of unit 10 final usage = 1.01272 1922 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2305 with usage 1.01271 . Total units tried = 395\n",
      "all done trying to reduce usage of unit 2305 final usage = 1.01271 1924 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2395 with usage 1.01271 . Total units tried = 396\n",
      "all done trying to reduce usage of unit 2395 final usage = 1.01271 1927 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2769 with usage 1.0127 . Total units tried = 397\n",
      "all done trying to reduce usage of unit 2769 final usage = 1.0127 1929 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 835 with usage 1.0127 . Total units tried = 398\n",
      "all done trying to reduce usage of unit 835 final usage = 1.0127 1931 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2161 with usage 1.01269 . Total units tried = 399\n",
      "all done trying to reduce usage of unit 2161 final usage = 1.01269 1933 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2129 with usage 1.01268 . Total units tried = 400\n",
      "all done trying to reduce usage of unit 2129 final usage = 1.01268 1936 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1735 with usage 1.01268 . Total units tried = 401\n",
      "all done trying to reduce usage of unit 1735 final usage = 1.01268 1939 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2956 with usage 1.01268 . Total units tried = 402\n",
      "all done trying to reduce usage of unit 2956 final usage = 1.01268 1942 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2767 with usage 1.01267 . Total units tried = 403\n",
      "all done trying to reduce usage of unit 2767 final usage = 1.01267 1944 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2150 with usage 1.01267 . Total units tried = 404\n",
      "all done trying to reduce usage of unit 2150 final usage = 1.01267 1946 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2420 with usage 1.01265 . Total units tried = 405\n",
      "all done trying to reduce usage of unit 2420 final usage = 1.01265 1949 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2960 with usage 1.01265 . Total units tried = 406\n",
      "all done trying to reduce usage of unit 2960 final usage = 1.01265 1952 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2133 with usage 1.01262 . Total units tried = 407\n",
      "all done trying to reduce usage of unit 2133 final usage = 1.01262 1954 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2953 with usage 1.01261 . Total units tried = 408\n",
      "all done trying to reduce usage of unit 2953 final usage = 1.01261 1957 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2750 with usage 1.01259 . Total units tried = 409\n",
      "all done trying to reduce usage of unit 2750 final usage = 1.01259 1959 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 260 with usage 1.01259 . Total units tried = 410\n",
      "all done trying to reduce usage of unit 260 final usage = 1.01259 1962 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 11 with usage 1.01256 . Total units tried = 411\n",
      "all done trying to reduce usage of unit 11 final usage = 1.01256 1965 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1117 with usage 1.01254 . Total units tried = 412\n",
      "all done trying to reduce usage of unit 1117 final usage = 1.01254 1968 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2603 with usage 1.01253 . Total units tried = 413\n",
      "all done trying to reduce usage of unit 2603 final usage = 1.01253 1970 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2461 with usage 1.01253 . Total units tried = 414\n",
      "all done trying to reduce usage of unit 2461 final usage = 1.01253 1972 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 887 with usage 1.01253 . Total units tried = 415\n",
      "all done trying to reduce usage of unit 887 final usage = 1.01253 1975 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 899 with usage 1.01252 . Total units tried = 416\n",
      "all done trying to reduce usage of unit 899 final usage = 1.01252 1977 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1834 with usage 1.01251 . Total units tried = 417\n",
      "all done trying to reduce usage of unit 1834 final usage = 1.01251 1979 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 894 with usage 1.01251 . Total units tried = 418\n",
      "all done trying to reduce usage of unit 894 final usage = 1.01251 1981 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1582 with usage 1.01251 . Total units tried = 419\n",
      "all done trying to reduce usage of unit 1582 final usage = 1.01251 1983 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1725 with usage 1.0125 . Total units tried = 420\n",
      "all done trying to reduce usage of unit 1725 final usage = 1.0125 1986 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 805 with usage 1.01249 . Total units tried = 421\n",
      "all done trying to reduce usage of unit 805 final usage = 1.01249 1988 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2128 with usage 1.01249 . Total units tried = 422\n",
      "all done trying to reduce usage of unit 2128 final usage = 1.01249 1991 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2276 with usage 1.01247 . Total units tried = 423\n",
      "all done trying to reduce usage of unit 2276 final usage = 1.01247 1994 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 606 with usage 1.01247 . Total units tried = 424\n",
      "all done trying to reduce usage of unit 606 final usage = 1.01247 1996 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 904 with usage 1.01246 . Total units tried = 425\n",
      "all done trying to reduce usage of unit 904 final usage = 1.01246 1998 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2796 with usage 1.01246 . Total units tried = 426\n",
      "all done trying to reduce usage of unit 2796 final usage = 1.01246 2001 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 349 with usage 1.01246 . Total units tried = 427\n",
      "all done trying to reduce usage of unit 349 final usage = 1.01246 2003 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 254 with usage 1.01245 . Total units tried = 428\n",
      "all done trying to reduce usage of unit 254 final usage = 1.01245 2006 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 94 with usage 1.01245 . Total units tried = 429\n",
      "all done trying to reduce usage of unit 94 final usage = 1.01245 2008 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1082 with usage 1.01244 . Total units tried = 430\n",
      "all done trying to reduce usage of unit 1082 final usage = 1.01244 2011 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2677 with usage 1.01244 . Total units tried = 431\n",
      "all done trying to reduce usage of unit 2677 final usage = 1.01244 2013 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2275 with usage 1.01244 . Total units tried = 432\n",
      "all done trying to reduce usage of unit 2275 final usage = 1.01244 2015 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1135 with usage 1.01243 . Total units tried = 433\n",
      "all done trying to reduce usage of unit 1135 final usage = 1.01243 2018 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1286 with usage 1.01243 . Total units tried = 434\n",
      "all done trying to reduce usage of unit 1286 final usage = 1.01243 2020 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 814 with usage 1.01243 . Total units tried = 435\n",
      "all done trying to reduce usage of unit 814 final usage = 1.01243 2022 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 823 with usage 1.01242 . Total units tried = 436\n",
      "all done trying to reduce usage of unit 823 final usage = 1.01242 2024 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 103 with usage 1.01242 . Total units tried = 437\n",
      "all done trying to reduce usage of unit 103 final usage = 1.01242 2026 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1389 with usage 1.01241 . Total units tried = 438\n",
      "all done trying to reduce usage of unit 1389 final usage = 1.01241 2028 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 855 with usage 1.01241 . Total units tried = 439\n",
      "all done trying to reduce usage of unit 855 final usage = 1.01241 2030 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 267 with usage 1.0124 . Total units tried = 440\n",
      "all done trying to reduce usage of unit 267 final usage = 1.0124 2033 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1289 with usage 1.01239 . Total units tried = 441\n",
      "all done trying to reduce usage of unit 1289 final usage = 1.01239 2034 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2703 with usage 1.01239 . Total units tried = 442\n",
      "all done trying to reduce usage of unit 2703 final usage = 1.01239 2036 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 875 with usage 1.01238 . Total units tried = 443\n",
      "all done trying to reduce usage of unit 875 final usage = 1.01238 2039 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 268 with usage 1.01238 . Total units tried = 444\n",
      "all done trying to reduce usage of unit 268 final usage = 1.01238 2041 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2277 with usage 1.01238 . Total units tried = 445\n",
      "all done trying to reduce usage of unit 2277 final usage = 1.01238 2044 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 195 with usage 1.01238 . Total units tried = 446\n",
      "all done trying to reduce usage of unit 195 final usage = 1.01238 2047 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1845 with usage 1.01237 . Total units tried = 447\n",
      "all done trying to reduce usage of unit 1845 final usage = 1.01237 2049 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2671 with usage 1.01237 . Total units tried = 448\n",
      "all done trying to reduce usage of unit 2671 final usage = 1.01237 2051 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 801 with usage 1.01236 . Total units tried = 449\n",
      "all done trying to reduce usage of unit 801 final usage = 1.01236 2053 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2289 with usage 1.01233 . Total units tried = 450\n",
      "all done trying to reduce usage of unit 2289 final usage = 1.01233 2056 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2829 with usage 1.01233 . Total units tried = 451\n",
      "all done trying to reduce usage of unit 2829 final usage = 1.01233 2059 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 375 with usage 1.01232 . Total units tried = 452\n",
      "all done trying to reduce usage of unit 375 final usage = 1.01232 2062 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1599 with usage 1.01231 . Total units tried = 453\n",
      "all done trying to reduce usage of unit 1599 final usage = 1.01231 2063 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 231 with usage 1.01231 . Total units tried = 454\n",
      "all done trying to reduce usage of unit 231 final usage = 1.01231 2066 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 463 with usage 1.01231 . Total units tried = 455\n",
      "all done trying to reduce usage of unit 463 final usage = 1.01231 2068 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2934 with usage 1.01229 . Total units tried = 456\n",
      "all done trying to reduce usage of unit 2934 final usage = 1.01229 2071 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2581 with usage 1.01227 . Total units tried = 457\n",
      "all done trying to reduce usage of unit 2581 final usage = 1.01227 2073 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 274 with usage 1.01227 . Total units tried = 458\n",
      "all done trying to reduce usage of unit 274 final usage = 1.01227 2076 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 914 with usage 1.01227 . Total units tried = 459\n",
      "all done trying to reduce usage of unit 914 final usage = 1.01227 2078 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1823 with usage 1.01226 . Total units tried = 460\n",
      "all done trying to reduce usage of unit 1823 final usage = 1.01226 2080 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2740 with usage 1.01225 . Total units tried = 461\n",
      "all done trying to reduce usage of unit 2740 final usage = 1.01225 2082 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1693 with usage 1.01224 . Total units tried = 462\n",
      "all done trying to reduce usage of unit 1693 final usage = 1.01224 2084 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2479 with usage 1.01224 . Total units tried = 463\n",
      "all done trying to reduce usage of unit 2479 final usage = 1.01224 2085 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2505 with usage 1.01222 . Total units tried = 464\n",
      "all done trying to reduce usage of unit 2505 final usage = 1.01222 2088 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 945 with usage 1.01222 . Total units tried = 465\n",
      "all done trying to reduce usage of unit 945 final usage = 1.01222 2091 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 2283 with usage 1.01222 . Total units tried = 466\n",
      "all done trying to reduce usage of unit 2283 final usage = 1.01222 2093 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 173 with usage 1.01221 . Total units tried = 467\n",
      "all done trying to reduce usage of unit 173 final usage = 1.01221 2095 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 1650 with usage 1.01221 . Total units tried = 468\n",
      "all done trying to reduce usage of unit 1650 final usage = 1.01221 2097 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 698 with usage 1.0122 . Total units tried = 469\n",
      "all done trying to reduce usage of unit 698 final usage = 1.0122 2100 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04122\n",
      "starting to reduce usage for unit 944 with usage 1.0122 . Total units tried = 470\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[402], line 59\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m popHDlist:  \u001b[38;5;66;03m#finding all HDs with at least one cluster member on the boundary\u001b[39;00m\n\u001b[0;32m     58\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m OUU \u001b[38;5;129;01min\u001b[39;00m HDunitList[t]:\n\u001b[1;32m---> 59\u001b[0m         HDadjoinSet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m( \u001b[43mgetAdjoiners\u001b[49m\u001b[43m(\u001b[49m\u001b[43mHDunitList\u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m)\u001b[49m )\n\u001b[0;32m     60\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(HDadjoinSet\u001b[38;5;241m.\u001b[39mintersection(OUU2nbrSet) ) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m#the OUU or one of its overused 1-neighbors adjoins the complement\u001b[39;00m\n\u001b[0;32m     61\u001b[0m             ouuHDs\u001b[38;5;241m.\u001b[39mappend(t)  \u001b[38;5;66;03m#so we can shed the OUUc to the complement\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[185], line 648\u001b[0m, in \u001b[0;36mgetAdjoiners\u001b[1;34m(UNITLIST, UNITNBRS)\u001b[0m\n\u001b[0;32m    646\u001b[0m allNbrs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m()\n\u001b[0;32m    647\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m U \u001b[38;5;129;01min\u001b[39;00m UNITLIST:\n\u001b[1;32m--> 648\u001b[0m     allNbrs \u001b[38;5;241m=\u001b[39m allNbrs \u001b[38;5;241m+\u001b[39m UNITNBRS[U]\n\u001b[0;32m    649\u001b[0m adjoiners \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m( \u001b[38;5;28mset\u001b[39m(allNbrs)\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mset\u001b[39m(UNITLIST)) )\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m adjoiners\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  #using 5/24/24 new avoidedIsland method after initial OUUc cluster\n",
    "#updated to exclude VRA cores\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04\n",
    "print(maxSD,\" is default maxSD.  Enter updated value\")\n",
    "maxSD = float(input(\"e.g. 0.04 \"))\n",
    "stopMaxUse = 1.02  #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs + coreUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1. or u in coreUs:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OUUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists) #work from farthest HD in\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        #contig = avoidedIsland(HDunitList[t], unitNbrs, list( set(OUUc).intersection(set(HDunitList[t])) ) )\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        #if not contig:\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                #trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "                #contig = avoidedIsland(HDunitList[t], unitNbrs, [OUU])\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #if contig  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop and u not in coreUs:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    #contig = avoidedIsland(list(trySet), unitNbrs, [shedU])  #avoidedI is buggy\n",
    "                    if contig and cContig:  #if contig\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = ( set(unitNbrs[shedU]).intersection(trySet) ).difference(coreUset)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t]+coreUs and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...  \n",
    "                    #canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    canAdd = avoidedEnclave(list(trySet), unitNbrs, [unitNoToAdd])\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist+coreUs and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "f481e23f-6d2c-4592-923a-0af5b8e8caaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00178 1.00177 and their SDs are 0.10997 0.04122\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 7059\n",
      "working on HD 300 out of 7059\n",
      "working on HD 600 out of 7059\n",
      "working on HD 900 out of 7059\n",
      "working on HD 1200 out of 7059\n",
      "working on HD 1500 out of 7059\n",
      "working on HD 1800 out of 7059\n",
      "working on HD 2100 out of 7059\n",
      "working on HD 2400 out of 7059\n",
      "working on HD 2700 out of 7059\n",
      "working on HD 3000 out of 7059\n",
      "working on HD 3300 out of 7059\n",
      "working on HD 3600 out of 7059\n",
      "working on HD 3900 out of 7059\n",
      "working on HD 4200 out of 7059\n",
      "working on HD 4500 out of 7059\n",
      "working on HD 4800 out of 7059\n",
      "working on HD 5100 out of 7059\n",
      "working on HD 5400 out of 7059\n",
      "working on HD 5700 out of 7059\n",
      "working on HD 6000 out of 7059\n",
      "working on HD 6300 out of 7059\n",
      "working on HD 6600 out of 7059\n",
      "working on HD 6900 out of 7059\n",
      "all done checking HD and complement contiguity for all 7059 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "finalFailList = list()\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        finalFailList.append(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "d551a617-2f09-4cba-8b7c-433846f0549d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"VRAPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "87683f8a-0082-466b-88a5-ebf23bdfb5fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "Now converting unit lists to vtd lists for all HDs\n",
      "1.0 7059 sum HDweight and nHDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"VRApatchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)\n",
    "\n",
    "print(np.sum(HDweight), nHDs, \"sum HDweight and nHDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "8 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchedWithCutsVTDs  #patchedVTDs\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that VTDs are represented 1.0 across all units\n"
     ]
    },
    {
     "data": {
      "image/png": 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iV0bi4+OVnp6uoqKi0LZgMKiioiJlZmaeNL5fv376+OOPVVpaGjpde+21uuKKK1RaWsrbLwAAILJXRiQpJydHEydO1LBhwzRixAjNnz9fNTU1mjx5siRpwoQJ6tGjh/Lz85WQkKABAwaE7Z+YmChJJ20HAADtU8Qxkp2drX379mnOnDny+/0aPHiwCgsLQx9qLSsrU0wMP+wKAAAax2GMMbYncTqBQEBut1tVVVVyuVy2pwMAABqhsc/fvIQBAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgVZNiZOHChUpLS1NCQoIyMjK0YcOGBscuXrxYo0aNUteuXdW1a1f5fL5TjgcAAO1LxDGybNky5eTkKC8vT5s2bdKgQYOUlZWlysrKesevWbNG48aN0+rVq1VcXCyv16urrrpKe/fu/cGTBwAA0c9hjDGR7JCRkaHhw4drwYIFkqRgMCiv16s77rhDs2bNOu3+dXV16tq1qxYsWKAJEyY06jIDgYDcbreqqqrkcrkimS4AALCksc/fEb0yUltbq5KSEvl8vm8PEBMjn8+n4uLiRh3j66+/1tGjR9WtW7cGxxw5ckSBQCDsBAAA2qaIYmT//v2qq6uTx+MJ2+7xeOT3+xt1jJkzZ6p79+5hQfN9+fn5crvdoZPX641kmgAAIIq06rdp5s2bp6VLl+q1115TQkJCg+Nyc3NVVVUVOpWXl7fiLAEAQGuKi2RwUlKSYmNjVVFREba9oqJCKSkpp9z3j3/8o+bNm6d33nlHAwcOPOVYp9Mpp9MZydQAAECUiuiVkfj4eKWnp6uoqCi0LRgMqqioSJmZmQ3u98gjj+j3v/+9CgsLNWzYsKbPFgAAtDkRvTIiSTk5OZo4caKGDRumESNGaP78+aqpqdHkyZMlSRMmTFCPHj2Un58vSXr44Yc1Z84cvfjii0pLSwt9tqRz587q3LlzMy4FAABEo4hjJDs7W/v27dOcOXPk9/s1ePBgFRYWhj7UWlZWppiYb19wWbRokWpra/XLX/4y7Dh5eXl64IEHftjsAQBA1Iv4d0Zs4HdGAACIPi3yOyMAAADNjRgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArIqzPQGgpdUFjTbsPqDK6m+U3CVBI3p3U2yMw/a0EMW4T7W+5rrOT3ecM/W2bcq8TrXPifP8VYd1oKZW3To7leKyt94mxcjChQv16KOPyu/3a9CgQXryySc1YsSIBscvX75cs2fP1p49e3T++efr4Ycf1jXXXNPkSQONVbjlKz34j636quqb0LZUd4LyxvbX1QNSLc4M0Yr7VOtrruv8dMc5U2/bpszrVPtIOum8xh63pTiMMSaSHZYtW6YJEyaooKBAGRkZmj9/vpYvX64dO3YoOTn5pPHvv/++Lr/8cuXn5+unP/2pXnzxRT388MPatGmTBgwY0KjLDAQCcrvdqqqqksvlimS6aMcKt3ylqS9s0vfv4Ceaf9ENQ3nyQES4T7W+5rrOT3ec2y7vraff233G3bZNWf+p9mnME76jgeM2RWOfvyOOkYyMDA0fPlwLFiyQJAWDQXm9Xt1xxx2aNWvWSeOzs7NVU1OjN998M7Ttkksu0eDBg1VQUNCsiwFOqAsaXfbwu/WWv3T8H1uKO0HrZv7fGfESLM583KdaX3Nd56c7jiTFOKRgA8+Gtm7bpqy/MWs9neZcb2OfvyP6AGttba1KSkrk8/m+PUBMjHw+n4qLi+vdp7i4OGy8JGVlZTU4XpKOHDmiQCAQdgIisWH3gVP+YzSSvqr6Rht2H2i9SSGqcZ9qfc11nZ/uOFLDIRLJ5TS3pqy/MWs9HRvrjShG9u/fr7q6Onk8nrDtHo9Hfr+/3n38fn9E4yUpPz9fbrc7dPJ6vZFME1BldeP+MTZ2HMB9qvU113XeXLdJa9+2TVl/c86xNdd7Rn61Nzc3V1VVVaFTeXm57SkhyiR3SWjWcQD3qdbXXNd5c90mrX3bNmX9zTnH1lxvRDGSlJSk2NhYVVRUhG2vqKhQSkpKvfukpKRENF6SnE6nXC5X2AmIxIje3ZTqTlBD73Y6dPxT4yN6d2vNaSGKcZ9qfc11nZ/uONLxz4ycabdtU9bfmLWejo31RhQj8fHxSk9PV1FRUWhbMBhUUVGRMjMz690nMzMzbLwkrVq1qsHxQHOIjXGEvsL2/X+UJ/4/b2x/PmiIRuM+1fqa6zo/3XEckm4d1fsHX05za8r6G7NPY7T2eiN+myYnJ0eLFy/W888/r23btmnq1KmqqanR5MmTJUkTJkxQbm5uaPz06dNVWFioxx57TNu3b9cDDzygjRs36vbbb2++VQD1uHpAqhbdMFQp7vCXGlPcCXwFE03Cfar1Ndd1frrj5F7T/4y8bZuy/lPtU3DDUBXcMFSp7vrfgkm1tN6Iv9orSQsWLAj96NngwYP1xBNPKCMjQ5L04x//WGlpaVqyZElo/PLly3X//feHfvTskUceiehHz/hqL36IM/UXFRG9uE+1Pn6BNTp/gbXFfmfEBmIEAIDo0yK/MwIAANDciBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACr4mxPoDFO/EhsIBCwPBMAANBYJ563T/dj71ERI9XV1ZIkr9dreSYAACBS1dXVcrvdDZ4fFX+bJhgM6ssvv1SXLl3kcDTvH/Dxer0qLy9vl3/zhvWzftbP+ll/+1t/a67dGKPq6mp1795dMTENfzIkKl4ZiYmJ0TnnnNNix3e5XO3uzvhdrJ/1s37W31615/W31tpP9YrICXyAFQAAWEWMAAAAq9p1jDidTuXl5cnpdNqeihWsn/WzftbP+tvf+s/EtUfFB1gBAEDb1a5fGQEAAPYRIwAAwCpiBAAAWEWMAAAAq9p1jCxcuFBpaWlKSEhQRkaGNmzYYHtKEXvvvfc0duxYde/eXQ6HQ6+//nrY+cYYzZkzR6mpqerYsaN8Pp8+++yzsDEHDhzQ+PHj5XK5lJiYqJtvvlmHDh0KG/PRRx9p1KhRSkhIkNfr1SOPPNLSSzut/Px8DR8+XF26dFFycrKuu+467dixI2zMN998o2nTpumss85S586d9Ytf/EIVFRVhY8rKyjRmzBh16tRJycnJuvvuu3Xs2LGwMWvWrNHQoUPldDrVp08fLVmypKWXd1qLFi3SwIEDQz9clJmZqbfffjt0fltee33mzZsnh8OhO++8M7StLV8HDzzwgBwOR9ipX79+ofPb8tpP2Lt3r2644QadddZZ6tixoy6++GJt3LgxdH5bfvxLS0s76fZ3OByaNm2apCi8/U07tXTpUhMfH2+effZZ88knn5hbb73VJCYmmoqKCttTi8iKFSvMfffdZ1599VUjybz22mth58+bN8+43W7z+uuvmw8//NBce+21pnfv3ubw4cOhMVdffbUZNGiQ+eCDD8y//vUv06dPHzNu3LjQ+VVVVcbj8Zjx48ebLVu2mJdeesl07NjR/OUvf2mtZdYrKyvLPPfcc2bLli2mtLTUXHPNNaZnz57m0KFDoTFTpkwxXq/XFBUVmY0bN5pLLrnEXHrppaHzjx07ZgYMGGB8Pp/ZvHmzWbFihUlKSjK5ubmhMbt27TKdOnUyOTk5ZuvWrebJJ580sbGxprCwsFXX+31vvPGGeeutt8ynn35qduzYYe69917ToUMHs2XLFmNM2177923YsMGkpaWZgQMHmunTp4e2t+XrIC8vz1x00UXmq6++Cp327dsXOr8tr90YYw4cOGB69eplJk2aZNavX2927dplVq5caXbu3Bka05Yf/yorK8Nu+1WrVhlJZvXq1caY6Lv9222MjBgxwkybNi30/3V1daZ79+4mPz/f4qx+mO/HSDAYNCkpKebRRx8NbTt48KBxOp3mpZdeMsYYs3XrViPJ/Oc//wmNefvtt43D4TB79+41xhjz1FNPma5du5ojR46ExsycOdP07du3hVcUmcrKSiPJrF271hhzfK0dOnQwy5cvD43Ztm2bkWSKi4uNMcdjLiYmxvj9/tCYRYsWGZfLFVrvPffcYy666KKwy8rOzjZZWVktvaSIde3a1fz1r39tV2uvrq42559/vlm1apX50Y9+FIqRtn4d5OXlmUGDBtV7XltfuzHHH4Muu+yyBs9vb49/06dPN+edd54JBoNRefu3y7dpamtrVVJSIp/PF9oWExMjn8+n4uJiizNrXrt375bf7w9bp9vtVkZGRmidxcXFSkxM1LBhw0JjfD6fYmJitH79+tCYyy+/XPHx8aExWVlZ2rFjh/73v/+10mpOr6qqSpLUrVs3SVJJSYmOHj0atv5+/fqpZ8+eYeu/+OKL5fF4QmOysrIUCAT0ySefhMZ89xgnxpxJ95W6ujotXbpUNTU1yszMbFdrnzZtmsaMGXPSPNvDdfDZZ5+pe/fuOvfcczV+/HiVlZVJah9rf+ONNzRs2DD96le/UnJysoYMGaLFixeHzm9Pj3+1tbV64YUXdNNNN8nhcETl7d8uY2T//v2qq6sLuxEkyePxyO/3W5pV8zuxllOt0+/3Kzk5Oez8uLg4devWLWxMfcf47mXYFgwGdeedd2rkyJEaMGCApONzi4+PV2JiYtjY76//dGtraEwgENDhw4dbYjmN9vHHH6tz585yOp2aMmWKXnvtNfXv379drF2Sli5dqk2bNik/P/+k89r6dZCRkaElS5aosLBQixYt0u7duzVq1ChVV1e3+bVL0q5du7Ro0SKdf/75WrlypaZOnarf/OY3ev755yW1r8e/119/XQcPHtSkSZMkRed9Pyr+ai9wOtOmTdOWLVu0bt0621NpVX379lVpaamqqqr0yiuvaOLEiVq7dq3tabWK8vJyTZ8+XatWrVJCQoLt6bS60aNHh/574MCBysjIUK9evfTyyy+rY8eOFmfWOoLBoIYNG6a5c+dKkoYMGaItW7aooKBAEydOtDy71vXMM89o9OjR6t69u+2pNFm7fGUkKSlJsbGxJ32yuKKiQikpKZZm1fxOrOVU60xJSVFlZWXY+ceOHdOBAwfCxtR3jO9ehk2333673nzzTa1evVrnnHNOaHtKSopqa2t18ODBsPHfX//p1tbQGJfLZf1BPz4+Xn369FF6erry8/M1aNAg/fnPf24Xay8pKVFlZaWGDh2quLg4xcXFae3atXriiScUFxcnj8fT5q+D70pMTNQFF1ygnTt3tovbPzU1Vf379w/bduGFF4beqmovj39ffPGF3nnnHd1yyy2hbdF4+7fLGImPj1d6erqKiopC24LBoIqKipSZmWlxZs2rd+/eSklJCVtnIBDQ+vXrQ+vMzMzUwYMHVVJSEhrz7rvvKhgMKiMjIzTmvffe09GjR0NjVq1apb59+6pr166ttJqTGWN0++2367XXXtO7776r3r17h52fnp6uDh06hK1/x44dKisrC1v/xx9/HPaAtGrVKrlcrtADXWZmZtgxTow5E+8rwWBQR44caRdrv/LKK/Xxxx+rtLQ0dBo2bJjGjx8f+u+2fh1816FDh/T5558rNTW1Xdz+I0eOPOmr/J9++ql69eolqe0//p3w3HPPKTk5WWPGjAlti8rbv9k/Ehslli5dapxOp1myZInZunWrue2220xiYmLYJ4ujQXV1tdm8ebPZvHmzkWQef/xxs3nzZvPFF18YY45/tS0xMdH8/e9/Nx999JH52c9+Vu9X24YMGWLWr19v1q1bZ84///ywr7YdPHjQeDwec+ONN5otW7aYpUuXmk6dOln/atvUqVON2+02a9asCfuK29dffx0aM2XKFNOzZ0/z7rvvmo0bN5rMzEyTmZkZOv/E19uuuuoqU1paagoLC83ZZ59d79fb7r77brNt2zazcOHCM+LrjbNmzTJr1641u3fvNh999JGZNWuWcTgc5p///Kcxpm2vvSHf/TaNMW37OrjrrrvMmjVrzO7du82///1v4/P5TFJSkqmsrDTGtO21G3P869xxcXHmoYceMp999pn529/+Zjp16mReeOGF0Ji2/PhnzPFvgfbs2dPMnDnzpPOi7fZvtzFijDFPPvmk6dmzp4mPjzcjRowwH3zwge0pRWz16tVG0kmniRMnGmOOf71t9uzZxuPxGKfTaa688kqzY8eOsGP897//NePGjTOdO3c2LpfLTJ482VRXV4eN+fDDD81ll11mnE6n6dGjh5k3b15rLbFB9a1bknnuuedCYw4fPmx+/etfm65du5pOnTqZn//85+arr74KO86ePXvM6NGjTceOHU1SUpK56667zNGjR8PGrF692gwePNjEx8ebc889N+wybLnppptMr169THx8vDn77LPNlVdeGQoRY9r22hvy/Rhpy9dBdna2SU1NNfHx8aZHjx4mOzs77Dc22vLaT/jHP/5hBgwYYJxOp+nXr595+umnw85vy49/xhizcuVKI+mkNRkTfbe/wxhjmv/1FgAAgMZpl58ZAQAAZw5iBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABg1f8DisPizpphuoUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"checking that VTDs are represented 1.0 across all units\")\n",
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "plotVTDs, plotUses = list(), list()\n",
    "for v in range(nVTDs):\n",
    "    if tractPop[v] > 0  and v not in allCutVTDs:\n",
    "        plotVTDs.append(v)\n",
    "        plotUses.append(vtdUSE[v])\n",
    "plt.scatter(plotVTDs, plotUses)\n",
    "plt.show()\n",
    "for i,v in enumerate(plotVTDs):\n",
    "    if plotUses[i] > 0.1 and plotUses[i] < 0.9 :\n",
    "        print(v,tractPop[v], plotUses[i],\"vtd no, its pop, and usage across all units\")\n",
    "        plotPoly(tractGeom[v])\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8SVAKlasZilr+txRFgW++CVYr5/v2JXnaNC68/gYXhwwl46efOFG/AWk//ujsEIUQ4oGRT8LCxG6/+oOMoBRF3q2fweupVmRv3caV8V+QtWkTGn9/QkZ+DMDlT8aQExeHccUKFMe/FSGEeDRJFU9hIpNki7zQTz7BPSYG94YN0QUFAfmTZy8N/wAlL49zL/QAIHjYUNxjYjDHn8ejSWOZtySEeORIglKIyCRZoXZxwee55254TOPjQ7HPPsWalobl/AXS5s/n8idj/vcaT08i16xG4+39oMMVQoj7RhKUwkQmyYp/4N22reNn1+qPYb10GX2pCPIOHSJ58rekzptHYP/+ToxQCCEKliQohYnjEo8kKOKfeT/9tONnz6ZNUSwWkr/+BltqGiHD33diZEIIUXBkkmyhIgmKuHOBb7wBQNr8+WT8/ruToxFCiIIhCUph4hhBkf8t4vaptFpK//4bAIlvvY3pxAknRySEEPdOPgkLkWulozKAIu6UoUwZQkZ8CMDFDz50cjRCCHHvJEEpTGSSrLgHvp074xIVRe7evdgyMpwdjhBC3BNJUAoTmSQr7lHwsKEAnGz2hJMjEUKIeyMJSqEiCYq4N241amCoVBF7VhZpCxc6OxwhhLhrkqAUJjKCIgpAxNXE5NKIj7CbTE6ORggh7o4kKIWJ4piE4tQwxMNNrdcT/MFwAJK/meTkaIQQ4u5IglKIOFrdqyVBEffGr2tXAFKmTcOalubkaIQQ4s5JJ9nCRC7xiAJkqFAB09GjnKgbg65YMVyiovJvlSvjElUJra+vs0MUQoh/JAlKYXItMXFc6hHi7pX6aRmW+HjyDh8m9/Bh8g4dJmX6dOyZmQDoihe/LmmJwjUqCo2Pj3ODFkKIqyRBKURU6vwrbtcatglxL1QqFfqSJdGXLInXU08B+f+2LPHx+QnL4SPkHTpEyrRp2LOyANCVKOFIWlwrR+FSqZIkLUIIp5AERYgiRKVWo4+IQB8R4Vh0ULHbMZ87l5+wHD6cn7RMnYo9OxsAXVjY1aSlEq6VK+cnLd7ezjwMIUQRIAlKYSKXdoQTqNRqDKVKYShVCu9nrktazp7LT1iuJS3fTsGekwOALjw8P2G5NqelUiU0Xl7OPAwhxCNGEpRCRJEyY1FIqNRqDKVLYShdCu/WzwDXkpazVxOWw+QePkTy5G//l7SUDM9PWKKicInKn4ir8fR05mEIIR5ikqAURpKfiEIoP2kpjaF0abxbtwZAsdkcSUvuoUPkHT5CZuwGlKtJi75kyZuqhzQeHs48DCHEQ0ISlMJELvGIh4xKo8FQpgyGMmXwbtMGuJq0nDlzNWnJv0SUuX49Sm4uAPqIiBuqh1wqSdIihLiZJCiFiaMNigyhiIeXSqPBEBmJITIS77ZtgatJy+nTjnLnvMOHyVy7FiUvDwB9qVI3VA8ZKlZC4+HuzMMQQjiZJCiFkSQo4hGj0mgwlC2LoWxZaNcOAMVqxXT6tKPcOe/wYTLXrMlPWlSq65KWq9VDFSuidpekRYiiQhKUQkUu8YiiQ6XV4lKuHC7lysGz7YCrScup047KobzDh8lctQrFZMpPWkqXvrF6qEIFSVqEeERJglKYSKt7UcSptFpcypfDpXw5aP8scC1pOXX10tAhcg8fJnPlnyhmc37SUqb0/6qHriUtbm5OPhIhxL2SBKUQUWw2IH84XAiRLz9pKY9L+fLQoT0AisWSn7RcVz1kXLEyP2lRqzGUKY1LpeuSlooVULu6OvlIhBB3QhKUwsR+dQRFLYtMC/FvVDodLhUq4FKhAj4dOgBXk5aTJ29MWv74A8ViuZq0lPnfRNwqlXGpUkW+DAhRiEmCUpgoV9fgkQRFiDum0ulwqVgRl4oV8XnuOQAUsxnTyZM3VA8Zf/8dxWJBFxaGX/dueLdvLw3lhCiEJEEpRK4tEihlxkIUDJVej0ulSrhUqgTPPw/kJy25Bw6QtnARl8eOI+mrifi0a4dv9+4YSpdycsRCiGvkq3phcu0Sjww7C3HfqPR63GrWpPi4sUSuXYt/r54YV67k9FNPEd/nFbI2bpQVxYUoBCRBKUzkEo8QD5QuOIjA118ncv06Qj8dgy0lhfOv9OV0q6dInTsPW1a2s0MUoshSKUrh6q9uNBrx9vYmIyMDryKyOmr2zp2kzZ1Lzs44bFlZVNi/D5VWrr4J8aApikLu3r2kzp1L5qrVqF1c8O7QHr9u3dCXLOns8IQo1Ar681sSFCcznzvHqWdaYyhTBs+mTXGvXw+36tWdHZYQRZ7l0iXSFvxA+o8/YsvIwKNhQ3x7vIB7TIzMExPiFgr68/ueriV8+umnqFQq3nzzzRse37ZtG02bNsXd3R0vLy8aNmxI7tWFwsSNUufMRePmRsSihQS+PkCSEyEKCV1ICEGDBhIZu57QUSOxXLrE+d4vc/qZ1qQtXOiYp2LPy8OaloZitTo5YiEeLXd9HSEuLo6pU6dStWrVGx7ftm0bLVu2ZOjQoXz99ddotVr279+PuojPq8hcvx7jr79hz83Fo1EjvJ5qhdrDg5y4OFyiq6E2GJwdohDiFtQuLvh06IB3+/bkxMWROmcOl0Z8xKURH+Faowa5u3c7nuvdvj0qrRaVTodPx45ogwLR+vretE9FUVDy8m5oHpe9fQf2nBwMZSPRh4U9kGMTojC7q0s8WVlZVK9encmTJzNq1Ciio6P58ssvAahTpw7Nmzdn5MiRdxXQo3iJJ33pMi6+9x4ulSqh9vIiZ+dOUBTUrq7Yc3Io/uWXeLV80tlhCiFuU87evWT8/AvpixbdtM1QvjymY8cc93Xh4QT0feV/DeUUhcS33sa4YgXuMTGYL5wHixVLYqLjNRGLFuJardr9PxAhClChmIPSs2dP/Pz8mDBhAo0bN3YkKElJSQQHBzNx4kR++OEHTp06RYUKFRg9ejT169e/5b5MJhMmk8lx32g0EhYW9sgkKLb0dE61bo1rtWqU+PprVCoVlkuXyNq4EUtCIvqICLzbtZVr2kI8hOwmEyq9ntw9e7AZjbjXq4daryd7+3auTPgSz+ZPkDz1O+yZmYRN/x732rVJX7aMSx98COQ3l/Nq3RqNlxe64sXxbNaUk02boQkMQBccgj0zE+9n2+HTqZNjJMZy+TIqjQZtQIAzD12Imzg9QVm4cCGjR48mLi4OFxeXGxKU7du3U7duXfz8/Bg3bhzR0dHMmTOHyZMnc+jQIcqWLXvT/kaMGMFHH3100+MPY4KSs2cPKd9PR+3igutjj+FWswYX3x9O3uHDlF7xB4ZS0gRKiKLGuPJPEv42Tw+g/L69YLfftLBh+k8/c+mDD/Jb9F+l0uvx7d4dxWIhfeFCFIsFz1YtCRwwAEPp0vf7EIS4LU5NUM6fP0/NmjVZvXq1Y+7J9QnK1q1bqVevHkOHDuWTTz5xvK5q1ao8/fTTjBkz5qZ9PiojKOlLl3Lxgw8xREaidnUl9/BhuPoHxrdrV0I+GO7kCIW4O4MHD2br1q1EREQwY8YMdDodALm5uXTs2BGj0YhWq2XBggUEBwczadIkZs+e7Xhthw4d+PDDD9mwYQMAe/bsYdOmTVQrIpcwFIuFzNWrseeZsCQmoiteHLca1dGHh//ja2xZ2ag0alQGA3mHj3Bx2DBMJ06gDQnBrdbjaP38yVy9GktiIj7PPYdvt664VKiQ/352O7l79qDx8cEQGfmgDlMI5yYoP//8M88++yya6zqd2mw2VCoVarWaY8eOERkZydy5c+nevbvjOZ06dUKr1TJ//vz/fI+HbQ6KLSubrPXrSHznXdxq1iR81kxUWi32vDxyDxzAnpWNR6OGsiiZeCjt37+fsWPHMm/ePEaPHk3p0qXp0qULAMuWLSMuLo4xY8Ywd+5czp8/z7Bhw4iKimL//v2YzWYaNGjA7usmkebk5FCrVi0OHTrkrEN6KCmKguX8eXQhIaj0eiC/eujy6E9IX7wYyJ/74lKhPDlxu7AkJqIyGCg5exaWS5fI2bUbt+qP4dGsmUzIF/dNQX9+31EVT7NmzTh48OANj7344otUqFCBwYMHU7p0aYoVK8ax6yaIARw/fpxWrVrdc7CFgflCAubTp7BcvETG8uXk/fUXSk4OhgoV8H+lj6PBmtrFBfdatZwcrRB35u+jJVu3bqVFixbk5uaycuVKTp8+zffff8+CBQuIjIxk4sSJ1KpVi8uXLzt+x4OCghxzzhITE1EUxTHH6vfff+fpp5922vE9rFQq1U0jLmoXF0I+/ghNgD9Kbh6Wy5cwnTqNR+NG6MLCufLll5ztnJ9MotGQNm8eAH49e+Ddrh0uFSs+6MMQ4o7cUYLi6elJ5cqVb3jM3d0df39/x+PvvPMOH374IdWqVSM6OprZs2dz9OhRlixZUnBRO4EtI4OUmTNJmTLV8Zh7/foE9nsVr6efRhca6sTohLh3+/fvJyEhgU2bNjF69GiWLFlCWloaoaGhrFixgqioKMLCwmjVqhXTp09n4MCB7Nq1i5IlS+Lm5sb27dsd+zp16hRarZYqVaqwbds2YmJiAFi8eDGDBw921iE+clQqFUFvvHHLbb6dO2E+dw6V3oAuNATzmTNc/vQzUmfPIXX2HNwbNsA9Jgb3ujG4lC/3gCMX4r8VeD/1N998k7y8PAYOHEhqairVqlVj9erVlClTpqDf6oHJPXSY8337ouTm4l6vHp5PtsCrZUs0D8ElKCFu17XREoCWLVsyc+ZMKlWqhNFoJDo6mh9//JGAgADS0tIICAhg9uzZ6PV6vL29URSFhIQEjEYjJ06c4NNPP2Xx4sWsWbMGo9HIL7/8gqenJ5s3byYhIQEPDw9mzZpFaGgoffv25eDBg9jtdkaOHEnz5s2dfCYeDWpXV8e8FACXSpUoOWc29uxsLn7wIcbffyd74yZUBgOR69eh9fNzYrRC3OyeE5TY2NibHhsyZAhDhgy51107Xd6RI6TOnk3GL8tRubhQ+uef0EdEODssIe6La6MlAN7e3qSmphITE8O4Tz+lVbFi7NyyBZPZjJe7Oxtmz2bczJm4GwycOXOGtLQ0SpcuTXx8PF5eXgx+911sVhsebu683L4HUz//hgD/ALQqDWtm/8b2Q2t57/3+jBv3Dv/3fy2IiHiJ9PRMnnvuDWo+PgsVKjw8yqPVejr5rDx61O7uFB8/jmJjP+dYjZq4VqsmX7ZEoSQr0v2D3IOHiO/VC42PD4GDBuHbtQsaDw9nhyXEfePj44PRaATyy/z9/PyIjo4mOCiI2t274+riws7vv+f5999n7OLFeJcsyZWUFKpUqkRAQAAJCQmUK1cOnU6HVq1FY9CiN+gZ/sVISpYMx83Nnbr1YnCJ8Kb4JRtLFq/m1MkraDRa5s2bRUBAWZKTs2jR/FUA+vdvS69ewzl69Ci9e/dGo9FQqlQpZs2aJX2DCoBKrcZQqhSW8+dJnTcPn+eek79xolAp2v3n/4ElKYnz//d/GCIjKb38FwJe6SO/uOKRFxMTw5o1awD4888/qVevHgDjJ07knQ8/5NW33yagdWuGDRtGekoKZpOJEr5+fDpkCOPGjcNmswEQFhbGhI/HEVEqgitXrqDRaniyZUs6d+lM/Pl4zDYL3875CUWBDRs28eKLLzFr1nzef/8jbDYb27ZtJzZ2PRMm5E/qnDx5Mh988AEbN25Eq9Wybds255ygR5Dfi72wpaWR9OlnJAwa5OxwhLiBjKDcQvqPi7GlpFBi+S+o3d2dHY4QD0R0dDTBwcE0aNCA8PBw3n77bfr27cvUqVPp2rUrnTp14sdZs7CaTHw/YwYrZsygeqOGvPfllxiNRipVqsS5c+f44IMP6PxcJ9KzMqhUqRJvv/02f/75JykpKbz66qu0aNECf/f8EZAGDRqQl5dHhQoV8PLy4rHHHiM1NZWuXbtw5vR5YmrXomHdOpzYv5et69exbOlS9uzYwR+rVzvmr1yrGtyxYweJiYn43mLtG3Fr7nXqoCteHNOJE7jXru3scIS4gSQof5OxfDmpc+bgWrMGWn9/Z4cjxAM1duzYG+5PnZpftebt7c3KlSvJ3roV1+rVUfLyyKhTh/1r17J9+3Y++eQTSpUq5egW/eZL/Vm9ewO9e/fGx8cHs9lMamoqPXr0oEePHnz6Xn9Wb9pNamoqCQkJmEwm9uzZw/fff0/FihUx5eXx5djXMXjm91QZN3kKSUlJtGndmudaNuejESOYMnWqI76zZ8/y0ksvSXJyB3IPHCDhzYEoFgul//hdOtKKQkcu8VwnZ88eEt8djFvNmhQfP97Z4QhR6BjKlyfj55/J++svqpQqha/FQky1auxbt44Wfn707dsXgE7PPMepU6cYMmQIw4cP5/nnn8fPz4/OnTvTtGlTvp29lMaNG3P48GE0Gg0nT56kcePGvPvuu6xYsYJOnTry5Tc/kpqayoYNG3j33Xfp3bs3AYGB6P2DWLt61Q1xLV68mOeff94Zp+ShlL7sJ851647G35+IHxdJciIKJUlQrqN2z59n4v3M0+iCg50cjRCFj9bfH5VOj2I2g0rN6H79WfXNJL5/9138mjT534iLlzcLFiygVq1abNq0iaNHj1KvXj0WLlzIunXrqFA2ggC//MqRiRMnUrx4cVauXEmlSpWoXLkyp8+c4cTJ80yZMoXw8HCqVKnCrl278PHxYevWrRgzs26Ia9myZXS4ulqw+G9J48ahWCyEjhopPZxEoSWXeK6TvWULAPbcXCdHIkTh5dOh/W0979/mtLR45nmmffsVjRs3JiMjg9q1a+Ph4UH79u2JiorCbDYzZFBnqtRsy2+//caQIUNIS0tjypQpdOrUiTKl/7fw5tmzZ3FzcyMoKOh+HfIjJ+jdd7j08UjOtG1H8Ylf4XW1/41isWA6dQpbaip5R49hTUrCv/dLaAMDnRyxKIokQbnKdOoUSePG4df7Jbzb394fYCHEvxvz3ig03npHWfC1EZboCHeqRlXgx19WOeavALz77rt4enpiNufSooE/l40BuLi4sOXqlwfI771kUOyO+0uWLJHLO3fIp107vFq25GyXriS+/Q7pj/+IvnRpsrdswXz6NAAqFxeUvDxsaakU++wzJ0csiqI7WizwQXDWYoFXJk0i5fvplNuxHfXVxbj+zZ2u8HrhwgX69etHZmYmDRs25KOPPirSK7yKR1vuoWTQqtF46LAm56J202E3WTGU8kbR2bh4cDkTF+1i+/bthIeHExoayvz583niiSeYMGEC3bp1IS35AjoXP/R6PXa7naNHj1K2bFlKhIRQrnx51sbG4uHhQUpKCitWrODQoUMMGzYMrVbL6NGjadiwobNPQ6Fnz84mbeFCsrfvwJKYiNbXF49mzfBs0hhd8eIcrZK/an3YtO/waNAAe3Z2/mKFarUsgCpuUuCf30ohk5GRoQBKRkbGA3k/u92uJE2apBypFKUkvPfebb1m3759Srdu3RRFUZRRo0YpCxYscGxbunSpMmTIEEVRFGXOnDnK6NGjFUVRlM6dOysXLly45f6ys7OVqKioezkMIQo1u8Wm2ExWJefgFSXt7H4l6cQGx7Zb/T5ZLNnK4Z1zbvn7tHP1SqVJkyaK3W5XduzYobz66quKoihKnTp1lLS0NCUrK0tp1KjRAz/GR1HK3HnK8QYNlSPlKyhHaz6uHClfIf9WKUo52627cnn8F4rp/HlnhykKiYL+/C6yk2QVq5XsnTs51/0Fkid+TUDfVwgdMeK2Xvv3NUuuH36OjIwkOzsbwLFmicVi4ezZs7z11ls0bdqUrVu33rA/WeFVPOpUWjVqvQaXKH90KUFkXTrp2Har3ydFUVCpVbf8fTpz9ixRUVGoVCqqV6/Opk2bALBarfj4+ODu7o7VaiU5OfnBH+gjxq97NyI3xFJ8whf4v9KH0NGjCBoyGK9WrUCrJeW77zj1RHOUwjUQLx4RRW4OiqIopC1YQPI3k7ClpaEvVYqwadPwaFD/tvdxqzVLrjEYDOzevRtfX1/y8vJo2rQpFSpUYO/evXz++eeYc7Jp1bIlFcuXR6vVMnnCOObOmsnuPXvZvGEDqGD4hyNo2bIlvXr14vDhw7i7u/P000/zzjvvFPj5EOJBUqlUuFULRr/rfz2G0tLS0FgTObJzOknnL3PmRBzH987HJzAKv5CyHDlyhMhSZbBZbUwdPR6v4JJs+eZ7jsZd4GzKIcfvn8FgID4+HoPBwKFDhxwJjbg3KpUqPyG5hYzly0l8dzCXR39C4OsDZE0fUaCKXIKSvWUrl0eOwrVGDQL798Otdu07vpZ6qzVLrlm0aBFVqlShVKlStGvXjokTJ3LmzBnKli1LvXr1+OSD9/Hw9GTdurUsXPQji5b/zulz5wgOCeHPP34nKzUFrcHg2N/MmTOpXLlywRy8EIWASqPCJfJ/DdV8fHy4nHCUSrVGkqvZTamyiUTVegWAKVOmULlsRcYPGcmJ9ATWxu3ksx4dePPyAHr260idmMepcHXF3q+++ooXX3wRX19fqlSpQkhIiFOOryjxat0aa2oqVyZ+TdoPP+ASFUXg66/jUb+es0MTj4Aid4kna8MGtIGBlJw3F/eYmLua6PVPa5bA1RGatDRatGhBQEAAgYGBxMXF4e/vj9FoJCggEIvFgpunF1k5OSSnpPDMM63Jyc3l6WfbM2DwUNLS04H8by59+vShefPm7N+/v0COX4jCQFGspKRsICVlA9WqBbNzzwn+2jWL35Yvuen3KSAgAA9/PwICAsjIyACgR48e/DjrV5599lkaN24MQI0aNVi7di1TpkwhNDQUT09ZCfl+U6lU+PfqRZkVKwgZPpy8Awc4//LL2LKy/vvFQvyHIlfFc6Z9B/SlS1N83Nj/fvK/eOeddxwVCDNnzmTAgAFMnTqVvXv30qlTJ/R6Pb6+vnzwwQfMnDmT1157jcGDB5N8+TIGNzcsFguKolCxYkXee+89SpYsib+/P1MnTyIuLo7vZ84iJSUFf39/jh49Ss+ePdmxY0cBnQUhCo+MjD28997n7Io7RKCfN8Pfac30RWeYOnUqGRkZtGzSAr2HAbuiMH36dMqVK0fnzp2JP5NA+UqRTJo0CTc3Nz799FP+/PNP3Nzc+Prrrykt3VEfuMzYWC68NgBdSAjhs2aiL1HC2SGJB6igP7+L1AiK3WzGdOIErgVQyqtWqx3/ValUjv4OWq0Wg8GA1WpFr9ejKAp+fn7s3bsXk8lEekYG5cqV4/Dhw1SpUoVt27bRr18/VqxYAUC71q05fOQvAPyvrgVUoUIFVCqVY7VYIR4l3t7V+eabhaxb/zvzF32HQhrDB73Lse0H0KHmkzeHEbthA5s2baJYYEnOHU7hs+GTWDTrF2bOnImbmxsAQ4YMYf369fz++++SnDiBOT6evAMHUGk0WC5cIGvtWmeHJB5yRSpByVofi2Kx4F63zj3tZ//+/SQkJLBp0yYqVKjAkiVLHNs2bNjA448/Tq1atejVqxfjxo2jXr16TJ48ma1bt9Lx2XZs374dgLZt29KmTRvWrFnDp59+iqIobN2+nVIRJQEc81ySkpIwm81opO+AeIRZLGkYXIJx9yjL0c278A4K5PfJa/ArUYFty05x9mAyedlmwiv5UTLKn7CKfv+9U/HAJL47mOTJ3+LRsAHFxn6OjzTPE/eoSE2SzVy9Gl1YGIbIyHvaz9/LImfOnEmXLl0A8NF6YE7PQa/TM2zwUHx9fCnlWZy05FT2r9tF1bJRzMteQuPGjbHZbEyfPp2srCxOnz5Nw4YN0Wk0TPhsDADdu3cnNTUVm83GuHHj7u3ghSjk1GodmcYDuPpocCt5hIun6tJuUBt0BknMHwYqFxfQ6fDp3BmPejJJVty7IpWg2DMz0RXAzP5/KzN+um5zZi2Zx8WLF/H08eK3n5YTXr4U7384nLYvPYc5J4+58+by1FNPOV4zYcIEhg0bxvvvv09mSjKmnPy+D8uXL7/nWIV4WHh6RuHpGUVAAJQp5+xoxJ0qPvZzEt59l8RBbxH2/fe4VpHqQ3FvitQlHn1EBKbjx1Hs9v9+8r/4tzLjeT8von79+hw+fJiPP/6YSTOmYDQamTp1KidOnGDtdz8yfPhwR2OjVatWsWnTJoYNG3ZPMQkhhDNpAwMp/sUXaIOCOPv886T9+KOzQxIPuSKVoKhcXECthqsLl92tfysztpmsjuZQ18oi1Wo1rq6uuLi44ObigtlsRlEUDh48yMiRI5kzZ45j0q0QQjystL6+lJw/D11YGOmLl/z3C4T4F0XqEo9Ko0axWh0rq96t6Ohojh49ire3N35+fhw6dMixjHy7p9pQq31D3n//fQD++OMPPDw8CAkJwdPTE7vVRvceL6BWq2nTpg1JSUmUKFGCrKwsYmNjqVaxQkEcqhBCOIU9KwtbSgquj0U7OxTxkCtSCYo+IgJ7ZiZnu3TFpVIlDJFl0IWFY7mYSM6OnVgvX8ajSRP8eryASvvPp2b//v2UK1eOnTt3Mnr0aJYvX+4oM96+dwe9e/dmzJgxzJ07l9jYWGJiYjhx4gTp6ekcWryG3uPeA+DMmTMA5OTkUKtWLerXr48x+cr9PxFCCFGALImJXHx/OPrSpbGlpmDPySFo4EBnhyUeckUqQfFq3RqAzPXryd6+nbRFi8BqBbUal8qV0fr7kzR2LOnLlhL8zjt4NGp0y/38WxWPxTeUI9s28ueBRLYeOkugtwcApUuXJjc3F5PFgotOy/nDB1Cp1KCC3/9cTcO6dUg8fpQcYwa+IcUewNkQQoiCkb1zJ9lbt5J9dSFUj2bN0F0tJBDibhWpBEWlUuHdpg3ebdoAoFgsWJOSUHt5obnaFjv34CGSxo7lfN//I+D1AQT263fTfv6tiiewcTWyZ33OoC75K3wO+uZHVh5IpHS1epSOLIfGbmfajGmElq2AggJ2hdWbPuStQYPwLxGGn70EBnf3B3A2hBCiYHg99RTmU6dJ+f57fJ7rQOjIkc4OSTwCivTMTJVOh654cUdyAuBapTLhs2dhKFsW019Hb/m6f6vi+XPhghuqeE78OZe6Jd1Z/+sPvDH3T76bvYoRIz5Co9Oh0xuw2O0cP3GCuvXqYXBzx8XD457nyAghxIOk1usJemsQ3u2fxfjnKmeHIx4RRTpB+ScqlQqNvz+mU6ewXE66aft/LRb49yoejUaDm6sr7zWvgluNMEcVD+RPor2+J4oQQjys3GvXxm40knf8uLNDEY8ASVD+QfDQodizs4nv0QNbZuYN26KjowkODqZBgwYcPnyYDh060LdvXwCaPfc8v/32G40bN2b48OEMGjQIDw8P2rdvT926dXmtVXP69+/vKCtevHgxHTt2fODHJ4QQBc2jYUMAUr6b5uRIxKOgyK1mfCfM8fGc6fAcHo0bU3zs57f1mnUpRpr6/3Pc61OMNPmX7UII8bDK2bOHc127EfLRR/h2ki9eRU1Bf34XqUmyd0ofHk7wsGFcHDoUbWAgfj1e+M9W+fsyc9CrVehUKvRqNVEeLuivjpbsMWZzPs/8IEIXQogHypKYSOLgIWhDQ/Fu19bZ4YhHgCQo/8Hn2XZYL10kecpUUmfOJHDQQAL69PnH5w8sGYxFUbDYFS6aLRzNzqOqpxt7jNnszsihZ3H/Bxi9EELcX4rVypVvviFlSn4vqPBZs1AbDE6O6u4oFgvWK9f1orpWsPD3/+bfufE/V7cpioItLQ2NpycqgyH/NVe3qXR6NB5SpXm75BLPbbJlZXF51GiMf/5J+bid/9rI7RqrXWHhpVTcNWrKuBmo6un2ACIVQogHw56dTXyfV8jdswf//+uLX8+eaH19nR3WXbGbTJzr1p28Q4fu6/u4x8Tg260rHo0bo9I8Wit1yyUeJ9F4eKAvUzr/H9Rt/qPSqlV0LyYjJkKIR1P2jh3k7tlDsfHj8H76aWeHc0+SPvsM0/HjFBs/Do23Dzi+u1/3Hf7aY1f/q/ztPuSPwmRv2oxLVBTawABQlPznKQq2tHTSly3lQv/X0BYLxbdTZ3yefw7tda0qxP9IgnIHlNxc7Hl5YLPBbYygCCHEo8ylUiVUej05O+Me6gTFuPJP0hb8QMiIDwvkOLyudhq/Fd9OHck9eIi0BQtInjSJ5G++weupVvh27YpL1arSB+s6UmZ8m0ynT5Ox/Fd0xYrd9giKEEI80ux2FLOZ3AMH/vOpit1O9s6dmE6ffgCB3T7z+fNcfP99PFu2xKdTpwfynq5VKlNszCdEbogl8M03yNm9h7OdOnP2uedJX7os/4uwkATldpjOnOFct+6oXAyETfpGMlwhRJFny8wkYeAgAILefONfn5u7fz9nO3UmvkdPTj/TmuSp3z2IEP+T3WwmYeAgNL6+hI78+IH/bdf6+uLfuzdl/lxJiSnfovH34+J773GycROSxo/HfCHhgcZT2EiC8h+yt24lvkdPUKmImDcPQ9myzg5JCCGc7vJnn5G7fz/60qX/cWFVgMy1aznb/QUUq5Wwad/h3bYtVyZM4OLwDx5gtLd2Zfx48o4do/gXX9yw5MmDptJo8GzcmPDvvqPMnyvxbtuWtIWLONWiBef7v0b21q0UsnqWB0KqeP6FNTmZE40a41I5iuKffYY+IsKp8QghRGFgTUvjRN0Yx/1iY8eSuWYN9twc7FnZaP390fj7YUlIJHvTJjybP0HxL75ApdOhKAqXx4whbc5cSs6dg9vjjzvlGNKX/cTFYcMIHjYUvx49nBLDv7Hn5JDx62+kzZ+P6fhx9KVK4dutG97t2qLx8HB2eLdU0J/fMoLyL9SurgB4P9NakhMhhLhK4+2NT8eOuEZHA5D4zjtkb9qESqtDV6wY1qQkcnbsxHzuHCEjP6b4l1+i0umA/H4hgQMGAJC9c+cDj12xWrk85lMuDhuGd4f2+L7wwgOP4Xao3dzw7dSRUr/8TMm5czCUL8/lMWM42bARlz7+GNPJk84O8b6TUpS/GTx4MFu3biUiIoIJHZ4Dmw21pwe5ubl07NgRo9GIVqtlwYIFBAcH06FDB5KTk8nLy2Ps2LE0bNiQESNGsHTpUvz9/alRowbjx4939mEJIUSBUanVhH78EQCmU6fI3bcPr6eecnyp+y+XPvoYlU6HR+PG9zHKm1lTU0kYOIicXbsIHjYM3xe6F/o5hSqVCrfHH8ft8cexXL5M+qJFpC36kbQFP+BWpw5+3bvl91R5BCtLZQTlOvv37ychIYGNa9dS4soVZvXrh1fr1ni3acOKFSuoXLkyGzZsoFevXkyfPh2AH374gQ0bNrBo0SI+/vhjx77GjBlDbGysJCdCiEeaoUwZfDp0uO3kJHXuPIy//UbAa6/hGhV1n6P7n9yDhzjz3HOYTpwgfMYM/Hq8UOiTk7/TBQcT+PrrRK5fR7GxY1Hy8rjw2gBOtmhB8nfTsKamOjvEAiUJynW2bt1Kg+LFOfN8R2qcOMnRihUo9vlnqNRqIiMjyc7OBiAtLQ0vL0hP30Vu7iEyMvaSmBhH2XLBGI0HMJku8/77Q2nUqBHr1q1z8lEJIUThkPbDD1wePRrfF17Av8/LD+Q9FUUhbeEiznXtitbPn1JLFuNeu9YDee/7Ra3X4936GSIW/kDEkiW416lL8jffcLJxExKHDCX34P3thvugyCTZ64waPhzvWbNoVakSeW++ySdz57JgwQIAcnNzad26NRcvXkRRFH7/43PCSuTPXG/SpCUnTpxkxoxvad68CcnJV9DpErDZKvHEE0+we/du9Hr9Az0WIYQoTGxZWZxs3ATPlk9SbNSoB/KedrOZyyNHkr54Cb5duxA0ZAjqR/RvsTUtjYylS0lb8AOWxERcqlXFr/sLeD3ZAtUDOmZpdX8f+QYFkePpha5YMS57WXB3t/Lmm73YufMAFoudli1bM3LkSJYsWcLYz+dw/vxUjEYjer0Lv//+By+//DJ//XWGpUsX4+IC8+f/RMWKFenZsycXL14EYMeOHSQmJuL7kK5XIYQQdyNl6lTsWVkE/N+rD+T9LElJJLz+BnmHDxM6ejQ+Hdo/kPd1Fq2vL/4vv4zfiy+SFRtL6rx5JL7zDpc//wzfjp3w6dQRXVCQs8O8IzKCcp19+/bxUZvWBOjU/Gmx4ucXQIUK5ZgzZyqtWz+Jt3cARmMmFy5c4sKFi7zwwkt88cUX9OzZk02bNmE0GqlcuTJ//PEHQ4f2Ye7cFajVai5duoSrqytnz57lpZdekss+QogixZ6by7HHqqPx9qbcju33/f1y9+3jwoDXQaWixNcTca1W7b6/Z2FkOnmS1PnzyfhlOYrFgleLFvh274ZrdPR9mX9T0J/fRS5Bub5KZ8aMGeiulr5dq9LZtnIlRrudZ9q2xWw2s2rVKmJiYkhPT+PMmTN4e3sTFhaG0ZhJfHw8Go2G3NxcihcvznPPPcfvv/9OTEwMO3bEcvToOapVq8aOHTsAGDt2LB4eHrz66oP5BiGEEIXB5bFjSZ0+g7Dp3+NRr959ex/T6TOkzZtL+uIluERFUXziVw/dqMH9YDMayfjpJ1LnL8ASH4/a3Z2IxT9iKF26QN9H+qDcg2tVOps2baJChQosWbLEse1alc6AyhV4rnxZatasScmSJXF1dSU2NpYlS5bi7x+A1WojL8/EY489RrFixfD29sbFxYWyZcuyYcMGjhw5woABz/DBBwPR6/WcOHGCWrVqsXTpUpYtW0aHDh2ceAaEEOLBsmVlkTp9BtrgYNxq1Cjw/SuKQvbWrZzv+3+cfuopjH+uwv/V/yN8zmxJTq7SeHnh17MnwUMGA2DPzsaanOzkqP5bkUpQtm7dSourq0y2bNmSLVu2OLZFRkaSZTRizMnDnJ6Ch+0M3qpLmE251Ktbla5d22OzmYkIK86BAwdYtHAh6WmpdGzXGp1WS0J8PM+/+CQ6vZann+7Hhg1/YbFYaNKkCbGxsXz44Ye4ubkRJL8wQogixPjb7wAEv/8eaheXAtuvYrORvnQpZ9q2I/6l3lguXSL0k0+IXLeWwH79HtnJsHfDcukSFwa8zoV+/XGvV48yq/7EvVbhr2QqUpNk09LSCA0NBcDb25vU62rGy5Yty8EtWzh86gxaby+m9X6TX1esoXjxPcRERfLVnF+xWm24GjwJCQnBbrcTWaY002bPJSsrmyvJyYwaOpHA4CBQq5k+fTp5eXn8/PPP1KpVC6PRSF5eHmFhYXh7exMQEMDKlStxKcBfWCGEKEzMFxK49NFHGCpWLPBLO2k/LOTyqFF4NG5M8LChuNWu/dD1NbnfFJuNtPnzufLlV6jc3Cg2fhxeTz310JynIpWg+Pj4YDQaAcjIyMDPz8+xbfbs2dQqGcHr6Rl84edLy2df4vLly+Tm5vL1nF+x2xXUKjWXky6TZ8pFp9Oxddt2rFYrGo0GtVpDelo6aalplAgvgaurKxaLBbPZzF9//YVWqyUkJAS9Xk9ycjJxcXGSnAghHmkaby9UOh3ezzyN2s2tQPedtW4t7vXrEzbl2wLd76Mi9+AhLn34IXl//YVP504EDRyIxsnr292pInWJJyYmhjVr1gAwcOBA1qxZwwsvvIDFYkFRFEo0qE+FyhU5cvAgcXFx5ObmUrJkSVRqFZ6entjsNtIz0rBYLGi1Op544glQqbDZbNjtNgAa1a3PlctXaNKkCWazGYCKFStiNpsxGAwkJydz5coVOnfu7LTzIIQQD4LG0xOXihVJGjuOrE2bC2y/9uxscuJ24dGwQYHt81Fhy8ri0qjRnO3UCcVuJ2LhD4R++OFDl5xAEUtQoqOjCQ4Opnr16pw/f54DBw5w9OhRlixZQteuXfljzRo67D+I1W7nk1GjGD58OPv27cNisWI0GqlTpw6urq6AiiD/EDZv3oz6uqEyDzcPigWEYrPZiI+Px8XFBVdXV2pfHXo0m8307t0blUpFYmIi27ff/3I7IYRwpmLjx+Nerx7n+/ThfL/+5Ozadc/7zN6xE8Viwb2BJCjXKIqCceWfnH7qadKXLiXo7bcptWTxQ11iXWTKjK8vL65duzZeXl706NGDLVu20L17d8LDw9FqtbzZpAnDR4/msrc3Hp6eJCQkYLdZKRPqi9oziEOH8lsIlylVljPnTqFWqbHarADotQa0Og2owGazYbPZ8PLyIiMjg+bNm7N69Wq0Wi0qlYp33nmHkiVL0rt37wI7RiGEKIwUuz2/zHXWLExnzhL+/TTc69S56/1d/OgjsrdspcyfKx+a+RT3k/lCApdGfkz2ho14NG1KyPvvoStW7IHHIWXGd+Hv5cWbNm1ynLx9+/bh6urKhg0bcHV1pf/kyZw0mUhOTkan09Hh2WdRKXaOXUghKSkJtTr/lP0y+9eryYkNvU6fP0JiNWFw0ZObm4vVaiUwMBAAg8GAt7c3r776Ku+99x5qtZpdu3ZRuoBr0IUQojBSqdX4dOhAqWXLULu5kfTFBBSL5a72pSgK2Rs34dGgQZFPThS7ndQ5czj9zDOYjh2nxDdfEzZ5klOSk/uhSEyS/Xt58apVqxyTZf39/UlOTuaxxx4jPj6e5g0acGjlSk5ZrVizjSxctAirLX9+SVJSEjqdDrvdToPWtR0jJ2ZL/lwTjVaLFjsGjQqbApcvX0atVlO/fn3WrVtHdnY2JpMJtVqNyWSiSZMmTjgbQgjhHCqdjmKfjObCwEGcbtsOn/bPoi9dGtfHHkN7m8t/mM+cwZKQUOTnn5gvXODi0GHkxMXh2707gW++icbD3dlhFagikaBcKy8ePHgwa9eu5eLFi6xatYoePXqwYcMGsrOzOXLkCFaLhV+W/0K0pxfhft6cu3QFm93u2E9ISAgAly5dwmKzolKp8ifXFi/OhYQEdHoXcnJMWBU1LgY9eWYzOp2Os2fPEhYWhouLC3l5eRw8eJDff//dWadDCCGcxvOJJwh5/33SFi4k+dsp2LOzQa1GV7w4Kp0OfcmS+beIiPxbqQi0QUGO0ZKsjRtR6fW4PQR9PO4HRVFIX7yYpE8/Q+PjQ/isWbjXqe3ssO6LIjEHZfLkyfzwww8cO3YMDw8Prly5gpeXF6VKleLYsWPYbTbyjEbybDbsgJtBj9lmx2AwoCgKJpMJ29VRlGvc3dyw2myYTCZUgKurK34eXnwwbAj7Dh7k3JG/iD14AK1WS2JiIm5ubixcuJCJEycSFxdHo0aNWLVqleOSkRBCFDWKomBNTCR7+3ZMx4+jWG2YL5zHfPYslgsJcPXvrsrV1ZG0mI4eRVeiBOHfT3Ny9A+e3WwmYeAgstauxfu5DgQPGYLGw8PZYTnIasZ3wd/fn127dhEUFMTFixd55pln2LJlC8nJyRgMBmxpaajVanJtNrRaLT7BoVy8cAGTyYRGo3EkJxq1BpvdhkqlIjsn539voFKRm5vHioVL8HL3ZORnn1MsJASdTodGo8HNzY1Ro0axatUqTp06RZcuXZgzZ46TzoYQQhQOKpUKXfHi+NxiCRDFYsF84QLms2cxnzuX/9+z51AsFryfbffgg3UyxWYjcfBgsjdtosSkb/Bs1szZId1/yj0YM2aMAihvvPGG47FGjRopwA23vn373vY+MzIyFEDJyMi4l9BuMGzYMMXT01MJCQlRtFqtolarFZVKpfTp00cJDAxU1KC4aTQ3xKxWqxXN3x77p1uAl7cCKCqVSmnRpLmiVqsd29zd3RVFUZT09HSlSpUqil6vV9RqtVKvXj3lzJkzSoMGDRRvb2/F09NTcXNzU6KjoxVFUZQPP/xQ8fDwUEqUKKEMGjSowM6FEEKIh4vdblcufvSxcqRiJSXjzz+dHc4/KujP77seQYmLi2Pq1KlUrVr1pm19+vTh448/dtx3K+AOgnfq0KFD1KhRg2PHjjlGQ1QqFdOnT8+/rqlSkfO3SziKomC/bv7Jv8mxWNBptPj4+bJu03r0ej3+3n54+npx4sQJLly4wLRp00hKSqJfv37ExsYSGhrKggUL2LhxIwCzZs3i559/5rHHHgMgPj6ecuXK0aRJE8aNG1eAZ0MIIcTDJGXKFNIWLCDko4/wulrwURTc1QSIrKwsunXrxrRp0/C9xcxrNzc3QkJCHLeC7GdyNxRFwcXFxTH3RK1WO+Z+1KhRA/t103AidDrHa26X3WbDYrMSExODp6cnGo2GQP8Ax1o/fn5+TJ8+nfT0dCZOnMiFCxfQaDQEBAQ49rF48WLS0tLo2LEjADt37iQtLY1Fixaxbt26ez4HQgghHj5pP/7Ila8mEvD6AHw7dXR2OA/UXSUo/fv35+mnn85v9X4L8+fPJyAggMqVKzN06FByrp+v4QSVK1fm+PHj6HQ6MjMzr66do86fDZ2ejouLi2OG+NmrtfluXobb3r/eJf+5GzdspHzZ8tSsUZPDx/8iKSnJMQfl/fffx8XFBUVRSE5OZteuXXTt2hWA9PR0EhISyMrKomLFimzcuJF27doxY8YMnnnmGd544w1H23whhBBFg3H1ai6N+Ajfrl0JePVVZ4fzwN1xgrJw4UL27NnDmDFjbrm9a9euzJs3j/Xr1zN06FDmzp1L9+7d/3F/JpMJo9F4w62gdezYkczMTOx2O4qiOBbx02q1xMfHo1arb2r4k2M03XJfKm5uDPTssy8AKrJzc/H0K8aBQ3/x+4xfqF+/Pjabje3bt5Obm0v9+vUZPnw4bm5uhIeHM3LkSAB++eUXSpcuTdu2bQH46quvePfddwFwd3enYsWKXLhwoQDPiBBCiMIsJy6OxLfexrNFC4LfG1Ykm9Ld0RyU8+fP88Ybb7B69ep/XIn3lVdecfxcpUoVQkNDadasGadOnaJMmTI3PX/MmDF89NFHdxj2nYmOjqZq1aps374di8XCoEGDGDduHHa7HVdXV0wmE3a7HRXgolKR+y+Xd1Tkz369/qfZsycBKtq0fobc3Fxyso10e/cVXHQq3NzccHNzY9myZRw7dozDhw8TEhKCi05HeloakH95Jysry3F55+TJk7Rv3x6j0UhSUhIAoaGh9+v0CCGEw/XLggQHB7Njxw4iIiKYNGkS3bp1w2g0olKp0Ov1jhYM7u7umM1mDhw4QJUqVbhy5QoZGRmEhIQwduxYhg0bhlarZfTo0TRs2NDZh1jo5R07xvl+/XGtXp1in3+GSqNxdkjOcSczan/66ScFUDQajePG1eoVjUajWK3Wm16TlZWlAMrKlStvuc+8vDwlIyPDcTt//nyBV/EoiqLs3btXiYqKUtzd3RWdTqeo1WolODhYMRgMikajUTw9PW+ozNFpVIqrXntbVTzXbl6BxW6q/PH19VVCQkKU6OhoJSAgQFGpVIpOp1Oq+HopW/u9oKSnpSnR0dFKjRo1boj3lVdeUSpVqqSEhIQoP/zwQ4GeCyGEuJV9+/Yp3bp1UxRFUfr376/Uq1dPURRFGTVqlPLmm28qQ4YMURRFUQYMGKA0bNhQURRFqV27tjJ48GBFURTH37Hk5GTFZDIpNWrUUOrUqaOkpaUpWVlZSqNGjR7wET18TOfPK8fq11dOP9tesWZmOjucO+LUKp5mzZpx8ODBGx578cUXqVChAoMHD0Zziyxv3759wD+PABgMBgyG25/vcbeio6Px9fXl4sWLuLm54enpyblz59DpdJQuXZrTp08D/xsXUVCRa7besI/AwECSr1xBATRqNahU2Gw2fDx9qBcawarLify0aiOvv9yDctGPc+HYAXQ6HXv37sVms1G1alUSEhJ47rnnGNYuijoVS4C3N3v37r0p3qlTp973cyKEENe7flkQT09Pzp8/T4MGDfD09MTDw8PRTVun03H48GEaNGjAvn37yMvLY+fOnVy6dAl/f3+0Wi09e/YEwGq14uPj4/g5OTn5hgIB8T/WlBTO934ZtasbYd9NLVRN2JzhjhIUT09PKleufMNj7u7u+Pv7U7lyZU6dOsWCBQt46qmn8Pf358CBAwwcOJCGDRveshz5QevSpQseHh706NGD3bt3M3PmTL755hvHtl27dpGdnEx6RgZWlZrHypfjbGICViAnJ4eMjAy0Oh1eXl6kp2dgu7oWT0ZWBlsuncGencVL3Z4n+VIiZ8+cQqfT4e/vn78MttFImTJl0Ov1uLu741azCxSCcyKEENdcWxYE4MKFC+Tm5rJp0yYGDRrEtm3bSE1NJSoqioyMDNzd3UlKSsJkMtG4cWOGDBlCxYoVOXToEGvXriUuLg7I/xIaHx+PwWDg0KFDpKWlSYJyC7asbM73/T9s2dlE/LAArZyjgl3NWK/Xs2bNGlq0aEGFChV466236NChA7/++mtBvs1di4mJYc2aNQD8+eef1KtXz7Ht4sWL5OXlERMRQSNfXxQgKTODqMhimE152Gw2LBYL/v7+juuv1/j6B2CyWtDr9Zw+fpSePXs6qoQURWHgwIGULFmSQ4cOUb9+fSIjIwtFwiaEENfz8fFxFCokJSU5VmSvXr068fHx1K9fn8OHD1O7dm3MZjP79u3Dx8eHv/76C39/f6xWKwMHDmT69Ols2rQJyJ/0/+KLL9K/f3+qVKniGIUR/6OYzSS8PgDzmTOET/sOfViYs0MqFO651X1sbKzj57CwMDZs2HCvu7xvoqOjCQ4OpkGDBoSHh/P2228THR3tuNyTmZHB3uRkcHFBp9WQkZ6GungAKrUGsFCyZEkCAgLYvXs3iqI4FgtMTb4CgFqtJigoCB8fH9RqDZ6eHnh5efHVV1/x2WefUb9+fXQ6Hbm5uc49EUIIcQsxMTF88cUX9OjRA6PR6OgHtXfvXgwGg2Pko1hwILm5OdSs8Rg5OTmoPdWs2rWKrKwsxo0bx5YtWxgwYAARERHUqFGDtWvXkpycTL9+/fD09HTmIRY6it1O4pCh5MTtImzaNFwqVnR2SIVGkVup7lqDNrVazYEDB6hcuTKbNm0iODiYnMxM1GoVZldXNGoVdtS8N2oclqu9UeLj49m1axdqtQqdTuf45b3W+C2iTFl27txJcnIyVqsFo9GI2WymWbNmpKenY7VaSUxMZP78+dSqVYulS5cC+U3hhg4dSrNmzWjcuDF5eXnOOTlCiCLt+i9xVquV8PBwQkNDOXLkCI0bN+azzz6jcePG/PbLErzdXdHosvEP8mfHhh289EJftFot3bt3Z/z48ZjNZo4fP05kZCQxMTH07NmTTz/91NmHWKgoisLlMZ9iXLGCYuPGPbKrEt+tIrFY4DX79+8nISGBTZs2MXr0aCZPnuyYEFaiRAkO7N6NTa3BNyCAvLxs0jNz6NChA66uruTl5aHRaFAUG8WLB5KcnJm/EqfVik6nw2KxcObkcWrXqZNfsqxWY7fbSU1NRafTUbt2bQICAsjNzaVLly6MGjWKBg0a0KFDB5YuXUpISAhr16518hkSQhR1Y8eOBfILHL744gsuXrzIJ598QqlSpZgxYwYmq42v3upKvD2TjMcu4Z/3PPvXbeGLCTP5v87tWb58OTqdjoYNG7J7924nH03hlr5oEWlz5xIy4kO8niw6LexvV5EaQbl+hnrLli05cuSIow1/kyZN0Gi1VNFoCC9RAn9fb9zd3cnLyyMnJwdXV1eeffZZdDoNLi6e+Pm5otFoHK3tdTod4eHhlCtbFo1GQ0zdugQFBWG32+nduzfPP/88HTp0oESJElitVjIzMx0z25cvX058fDyNGze+7z1hhBDidlw/mnL48GHat29Pq+dfYOeZVFq88DoH9qRwfLLC7h/+4MN3RpOVBxPGfkbr1q1p2rSp/C37D4rVSvLU7/Bu2wbfzp2dHU6hVKRGUK6foe7t7Y3NZnNMCKtRowZq4PeMDPjzTyqUKIlapcFqtVI8ND+pWLhwIRo1nD55Gr3eDZPJhMlkwtXdHU9fPxISE7mQkIDNaiVu1y5UgM1m4/vvplIqLIwvRo/k1JHDTJ8+nQXz5jJn3nwALl++TJUqVYiNjaVTp05s376dOnXqOOksCSFEvrFjx/LbgURsdoWfD1xm6pSp+HnoUatqsyp2KwBmm524M6k0LhsIZQMdk2PFv8tcswbrxYv4vfiis0MptB75EZTatWvj7e1N6dKlcXNzcyQk8fHxHDx4kJdffhk/Pz/ef/99GpQrR2U3d1zd3Dh6IR6L1UxkZCThEWFk5WQCoKChbYfn+Hja9+j0Brp3786ZU6cIDQxAp9Xi6uJC3bp16fbyywwaNIjy5ctz7vwFdh88xMARI/lh2U8EBATwwbvvMHz4cBRFwcfHh6ZNmwLQtGlTDh8+7LTzJYQQ12tSPoiS/u6UC/bkSpaJoxeNHE40sv98Ol+vO8HXa0/QtEKQs8N86KTOnoNbrVq4VKjg7FAKrUd6BOXHH38kKSmJjIwMmjdvzvbt29Hr9fTo0YMRI0YQFhaGr68v+/btY8uWLQwqEcZfWg0zvv+eMe+P4diFY5w5c4ZTp07h4eGBXq/HRe/G4kWL+OWnn4iuXp3fV6/h58hIcnNzKVGiBF26dCE1NZXlP/2MQaclKioKvV5PZGQkM2bMoFatWrRq1YqnnmjGtPk/oCgK9erVY9++fdSoUYN9+/Y5Wt4LIYSzuRu0RIf53HJboKeBYC+XIrlOzL3IPXiQ3L17KfHN184OpVB7pEdQfvrpJ5o3bw5Az549OXLkiOOa6rUKnLS0NFq3bk1kSCjLEi6QarPRr18/jsYfpU7ZWswfPMtRGhwYGIjJkssPP/xAcGAIxrQ0XuzejaBSpQgvVZrjx4+zceNGatSowefffE14eDj79u0jISGBHj164OHhQa9evdiyZQsder1E//79UavV9O7dm5UrV9KoUSNsNhtNmjRx5mkTQojbUjrQA3fDI/09975InTMXXYkSeMjf+n/1SP/LSklJISIiAshvtW80Gh0z1FNTUwkLC8NsNhMfH8/0Fq34ec9uzqcms2XdOtq3b8+B+EPEZe1DRX5Le3OWGZVKhclkokK5Suzev5NdcXGE+fmRabFQrkpVrlw4j1qt5tKVK6QlJ7Nr1y5GjBjBzz//zIQJE7DZbMTGxnLx6GFKRdcA8rvxLl682FmnSQghxANiuZyEceVKgt4aVHQXAbxNj/QIip+fHykpKUB+p9hrFTsAr732GkFBQSxevJjejRozctUK9is21Go1ffv25fnnnyfPksf3s7/HZs9frVMhvzlb3759Wb9pDU2aNGHOnDls3LgRrdXK4p276TdoEKdOnSIjI4OAgAAiIiI4e/YsAQEBrF27ltjYWFxcXJABUSGEKHrSFv6AWqfDp0MHZ4dS6D3SCcqzzz7r6C0yZ84cGjVq5NimKAq+vr6kX76M14EDZOg0ZNi1REZW5lx8ArEbdmK3Q4OOL+F5dcEmFw8XLBYLXj4+9KgbQ+yaNYx64038vbzxVqmocGAPS2bP4bGoKvioVCQkJNCyZUt27tzJzBnTqVX9MQa+2pcz+3aDXLMVQogixW4ykb5wEd4dOqCRjrr/6ZFOUDp16oSfnx/e3t4cP36cMWPGUPFqG+HPPvuM06dP88prr/HNlSSCvfVUqhJIr9e7k5Z6hYMHdhAVVZHHqkbw2NNPkp2dTUJiQn53xRIl6Nj7GdKzsli6cQNmxU6mSkXr4cNJTkvlozGj0Wu1RIYE8+1XE/D19aX1k0+yc89eElPTuZxnIaJadSefHSGEEA+S8bffsKWn49e9m7NDeSg80gkKQFxcHBkZGZw9exYPDw/++usvAMLDw0lPT2dAv35U9PbGlJJHU6OK9Yt/4cCBAzz55JNkZWXx4zeTOXvgMCXLlcHHx4egoPxyup5vj6FYsWLs2bOHxx57jIoVKxIbG0u1atXYuHEjT3XuzOmLl9i/aSP16sXQplVLQMqIhRCiKFIUhdTZc/Bo3Bh9yZLODuehoFKulbMUEkajEW9vbzIyMm6YM3I/2XNzGffSS9j37qUtKo6UjOBPg45XP/qICRMmsPXIQZJPnqFy5cq89tpr7N27l+NHN3DyWBLenh4c+OskWo2GeV8OJynpCq9+OBFUCqDi20nfcubYUbbt2o3ZZuP48eNcuXKFKlWqEBUVxYwZM9DpdADk5ubSsWNHjEYjWq2WBQsWEBwczKRJk5g9ezYAgwcPpsN11y5btmxJ5cqVGTdu3AM5V0IIIe5c9vYdxPfqRfjMGbjXrevscO6Lgv78fuRHUG6H2tWVFoMHc6RmTYp9/hmbL16k3K7deH0/AywWLh09gUdwEIFR5fjk0zFkZGTgU6siZy4ksfvQCUwWG16+/sTF5/Ls/70PKjULtizisYY1+HrSJN794EO8/fxQFIVatWrh7u7Onj17qFChAkuWLHHEsWLFCipXrsyGDRvo1asX06dPB2Dy5Mls3bqV2NhYPvnkE8fzt2zZ8sDPlRBCiDuXOmcOhrJlcZMu4bdNEpSroqOjCQ4N5ZmxY0mqEkWvKd8ydPGPlNy2nVF9+lC5VGlO7djL2fMXGDRoEHWfqcvWrVvp06cPXbp0oXPnzoSGhvLtt9/i7u7Op4M+5cuRX+Lj4+MoI96wYQOBgYHUq1cPyB/9uD7JiIyMJDs7G8jvz3JtafPSpUuTm5t7w/o9ABMnTuS11157cCdJCCHEHTPHx5O1fj1+PXtIU7s78Ej3QblT13qkXLPgSAyDWz1F0OLF9HuxB3sqV2LljNmUK1eOdfvWsdG0kZVbfycv2YhGreW7mW8T5eZBSvoTfPPVT9SrV5/KNWqx9nAi2quXcVbHbkJRFMpVfgx3HQQGBnLy5EkuXrzI8OHD2bt3L/Pnz8fX15dGjRoxffp0kpKS8Pb2xsfHh88//5yTJ0+ybdt2VN6hnEyzcj4thw3Hr9zx8aZmm/B109/0uEqlcjSy+7dfJovVTvWSvvi537wPIYQQ+VLnzUPj7Y3XM884O5SHiiQo/0IbEEDpXj2xbdlM2cU/oTp1hsvB+YsN/l/0/zFlyhSebtiEN994gbi4K6z5I4733nuPl376mkZ1a7Lopz+oVKkSl/fH0rVrV3JycjCmJBETE8NLb3/EoY2/88cffxAZGcnw4cNp2bIljRs3ZsSIESxZsoS4uDhWr15NvXr1qFChAtu2baNp06b07t2bVq2eQmNwJeXMYapUqUKjcoHAreexeHl50apVKwBycnKwWCzs3bvXcZx3O4/lsjGPpMw8SVCEEOIf2LKyyVi6DN/u3VG7uDg7nIeKXOL5D/Xq1WOXVkfomDGs2bKF0mvXcuq1V7FlZaEoCn6+3oCKgIAAMjIyUKvVmM1m6td6DHd3d1xdXR2Xcf744w8aNGjAyZMnAdi5cyeVK1fGYrFw9uxZlixZwoIFC9i6desN+zOZTDzzzDO4u7tjNpvZt28fV5Kv4O6eX5U0b948fv31V+DW81hcXV2JjY0lNjaWfv360a5dO8fxyTwWIYS4fzJ+/hl7Xh6+XTo7O5SHjoyg/Ifo6GiCg4Np/cV4wmLq8V6bNrz2+gDGoNB19Ge0bduCP/5Yh1brwfTp0/Hw8KB8+fJ8P38Zy9ftpEePHhw9ehSAxYsXM3jwYLp06UKXlvVxMeg5c+YML730Evv27SMuLo4BAwbw5JNPEh0d7difi4sLK1asIDY2lv79+7N9+3bGjhtPsQrVuXBoB1OmTKF169ZA/jyW2NhY4MZ5LNcsXrz4hpGSa/NYrr3mThWuGjAhhCg8FLudtHnz8GzRHF1IiLPDeehIgnIb/j43Zdze3WRu346XuztTZ36Cn96LwOKPO7a3adOGBo+V5b0xX7N7926SkpIAWLRoEVOmTOHFF1+kauPW2JPP8Nlnn/Hdd9+xb98+KleuzPr164mJiWH9+vVotVpycnIAOHjwoGP/n3zyCWXKlkNRFJo1a8aGDRsc28qWLcuRI0eIiopCURR27tzp2Jaens6lS5cczeo2btxItWrV8LjaKfdOyVQvIYT4Z9lbtmI+e5bQ0aOcHcpDSS7x3AXFxYA6KYUjMXUxjZ6CdeEf2M1mx/aYmBi27z4AwJ9//umo2oH8Zj3XRjWuXcZxdXXF39+f9PR0srOzMZlMaLX5ueMff/zBU089dcP7+/j4kJmZCUBGRgZ+fn6ObbNnz6Z+/focPnyYjz/+mJEjRzq2/fLLL7Rt29Zx/6uvvpIqICGEuE9S583FUKkirtWlc/jdkBGUuxA+fATZzVuRsXoVxh8Xk775ENaj8ZSY9C0qvZ7o6Gj8/bxp0KAB4eHhvP322/Tt25epU6fStWtXOnXqxOXZ8zCZTPR7ZzjLYnfT4rkexDRsgtVi4bkeL7MsdjegMGHSdzzbpSfLYnddfXcVuXofFv4wj1eHVePAxlsnQFabnUzFhb/OXXJU+EyZNZ9X3x3huL//8FGeeLodmRnppKem4B8ZTUzTJ2/7PGTlWalU7ME00xNCiIeJ+exZsjdsJHT0aCktvkvSSfYeWdPSyDt0iPOv9MW3c0dcKldBGxDAafNFqja/f5Oi3hj4Fn+s20ityuWYOXMmAwYMYOrUqWRkZNCpUyfOX8nAw6Bm7qyZlCtXjoyMDJo1a8auXbtu2ldsbCy//fabdKMVQogCcumTTzAu/5XI2PVFpnqnoD+/JUEpIGfaPE3e8dOO+4pGjaIzoIRHENCsMX5t25Czew+ggEYDNhuo1GiDgnCtWgXNHR5rRo6ZZXsSeLF+qZu27Y1Po7iPK0FeReOXQgghChN7djYnGjXGt2tXggYNdHY4D0xBf37LJZ4CUvzryeTs2oV7gwbYMzLI2rIFxWQm69gJUmbMJO3bb//xtYpGg2fHThQf8i5qg+G23s9qV1Crbz1smJ5j4bFw37s6DiEepMGDB7N161YiIiJua12qCxcu0K9fPzIzM2nYsCEfffSRY1+yLpUoLNJ/+QV7bq6UFt8jSVAKiL5kyf+tUBkUhKFsWQACAGtqKtmbNuFevz4ab2+w20GrRbFasSQkYFy1mqRvvmFfsokz7XryD3nHDXLNNqKKe99ym9VeqAbFhLil/fv3k5CQwKZNmxg9ejRLliyhS5cuwP/6+YwZM4a5c+cyffp0hg0bxjvvvMO3335L8eLFb9iX9PMRhYWiKKTNm4/nE0+gCw11djgPNUlQHgCtnx/e11XPXKPS6zGUKkVg31ewGI2UnjuPJ996GX1ExF2/V57Fhl4rxVmi8Nu6dSstWrQA8kc/Zs6c6UhQbtXP51pDw7feeoukpCRGjRpFTEwMcO/9fIQoKNlbt2I+fZrQj0Y4O5SHniQohUTIa/1JnzmTrM1b8LuHBEWnUWOXERTxEEhLSyM0NBSLzY63tzepqamObbfq53P2wkX27dvHvAU/4OZioE2bNsTFxd1zPx8hClLypMmo3d1xrVnT2aE89OSrdiGhdnVFCQgidX0sit1+1/vZfjqF2qX9/vuJQjiZj48PaenpvDpvN+sOnCVX5cqG41eIPZbEe59/Q8lK1flx9VbeGPw+w0d8xLJDaRQvWYrLNg+OZ2rJtcHaIxcZPvpzolt2Zt/5dPadT3f2YYkiLnfPHuzZ2VJaXAAkQSlEwkYMx7x1K2ffGIjtaiO2O5VnseGml4ExUfjFxMQwef7PPB7hR/LRnXR85gkalQukcfkgygZ5UKVMGEGeLnh4+RB/MZkXGpSjREgQFf015OZko8dOs0qhpF86z8Rh/Vjw9Scc3rLasS6VEA+a9eooYOCbbzg5kkeDfJIVIl5Nm1J8wnguDHufo0+2IuS1/vh16XzbmbiiKKglaxcPiejoaLI1Hnw/+AXKlIq4ZUPDn39ais1mY/r06fi46Xh98HBqNW6BVrFRrU1vbHaF/fv3A/n9fKbM/dGxLpUQD1r6jz+iMhjw6dTJ2aE8EqQPSiFkvpBA4lcTyf11OS4tnqT4u++gL1H8P1+3+1waYX6uBHlK/xPxcLDbFbacSmb3uTTefKLcvz535pYz1CrlR6VQL1QqFVkmKz/tTcBuV4gIcAcgIS2XrrXDH0ToQtxAsVg42ewJPBo1JPS6JUaKEumDUgToSxQnYuxnZDRtzIX3P+DUE39i9/PHLSoKjypRuERF4VKpEtqQEMfoisVmJznLRI2S0v9EPDzUahUNygZiu42J3aUC3Ikq9r/Seg+DlhfqlLyf4Qlx2zJXr8aalIRv9+7ODuWRIQlKIebdqhUeDRqQvWUrWQcPkbLvINnzf0CTkQ6A3csbnzat8Wvbhh3aQJqUD3JuwELcJV83vWONKMi/XGnQavB00eJh0OLhoqVwjfUKcaPUufNwq1ULl/LlnR3KI0Mu8TxkFEXBmpRE3uEjZO3cSerSZagzjeQGBBPapAH6iAh8u3ZF7erq7FCFuCcmq42sPCuZeVayTFZK+Lri46Z3dlhC3CT34CHOPv88Jb75Gs8nnnB2OE4ja/GIGyhWKzm7dpOx/BdMx46Td/w4hshIggcPxq12LSl1E0KI+yxx8BBy4uIos3oVKo3G2eE4jcxBETdQabW416mNe53aAOQdO0biO+8S36sXmsAAfDt2wqvlk+jLlEGllqpyIYQoSNbkZIx//EHgm28W6eTkfpBPrEeMS/nylPrlZ8JnzcKreQtSpk/ndOs2nG71FLkHDzk7PCGEuGODBw+mQYMGvPDCC1gsFsfjubm5tG7dmkaNGtGsWTMuX74MwPjx46lXrx5PPvkkFy9eBKBXr148/vjjNG7cmLFjxxZYbGk//ghaLT7PdSiwfYp8kqA8glQqFe51ahPywXDKbdlM2PTvUXt7E9+zJ9lbtzo7PCGEuG3XLypZoUIFlixZ4th2bVHJDRs20KtXL6ZPn86lS5f4/fff2bx5MyNHjmTkdSW/M2fOJDY2lnfeeadAYlPMZtJ/WIh3mzb5C8GKAiUJyiNO7e6OR716lJw5A9foasS/1JsLr7+Bct23ECGEKKz+vqjk9StXR0ZGkp2dDfxvUclz584RFRWFSqWievXqbNq0Ccj/4tanTx+aN2/uaO53r4yrVmO9cgW/7t0KZH/iRjIHpYhQu7sT9t13ZCz/lYvvv0/awkX4vSD1+kKI+8OYeQiLOfW/n/gfEhL2UL58BCkpG7HbL3Dx4lFSUjYC4OdnYv/+rVSoUApFUVi1agom02W2b19HYuIaNm/eS3LyJVJSNjJsWHv8/Lw5ceIcL73UhVWrptxzbMnTJ+BauyaGsmXveV/iZpKgFCEqrRaf9s+SNn8+Gb/8IgmKEOK+sZhT8PdvdM/7KVbsEIrigb9/Q86e3U1oaAX8/RsCMGXKFJo0eYYRI0awZMkSJk9ez2effcaAAel06TKS6OhoKlWqir9/Q/z98/fn7w863SR8fOqhuYdJrbkHD2L/KwH/SUPv+RjFrcklniIm9/Bh8g4fxlCmjLNDEUKI/xQTE8OaNWsA+PPPP6lXr55jm6IoBAQEABAQEEBGRgYAPXr0YMOGDTz77LM0btwYyC+BBUhKSsJsNt9TcgKQMmcGmtAAPK7uXxQ8GUEpYnTFimEoG0nGL7+g9vQk5P33nB2SEOIRZLPnFch+oqOjCQ4OpkGDBoSHh99yUcklS5Y4FpUE6Ny5M0lJSZQsWZJJkyYB0L17d1JTU7HZbIwbN+6eYrImJ5O5YjWBgwZJafF9JI3aiiDFZuPSyJGkL1xE2S2b0V4b+xRCFEqDBw9mwYIFmEwmmjdvzqxZs9DpdOTm5tKhQwd27NiB3W6ncuXKLFmyBIvFwosvvsjevXvx8PCgZ8+ePPfcc/Ts2ZNTp05RrFgxRowYQaf7uOpuUtJKgoJa/uN2my2XpKSV6PX/+/uj1wfg6VnpvsVUUK5MmkTytO8ot2GjVO9cp6A/v+USTxGk0mgI6NcPlcFAynfTnB2OEOIfDB48mOrVqzNv3jwaNGjAG2+8gcViYcGCBbRu3ZqaNWuya9cuunXrxsSJE/Hy8qJx48bUqFGD+Ph49u/fz6+//sq6deto3LgxJpOJnTt3EhQUxKRJk8jLK5hRjlvRaNz+cVuGcT+pqZvx8Xn86vyQ/JvZnHzf4ikoitlM6g9z8Wr9jCQn95lc4imiNJ6eKHY72qBAZ4cihLiFa/0/+vTpw3fffYe3tzctW7Zk//79LFy4kOjoaEaPHk3fvn2Ji4sjTKPhwM6dvPTss/yxZQunL1ygevXq5OXlMW3aND7//HOOHTtGyZIlCQsLIyUlhUOHDlGzZs37Er9O50tKyoZbbFFhtRoJDn7mpi1m85WbXpOYmHjDQpEqFTctHPn3x66t8HHjYyr+fsHg749dWxrk3x5TbT6ENjkD/xd63uLYREGSBKWIUun16MPDMf6xAr8ePVDpdM4OSQhxnWv9Py5cuEBISAjnzp3D29ub3NxcALKzsylbtixXLl/m/Pnz7N61C41Wy+7ERPYcPYpGpWLr2rU0bNiQsWPHUrJkSc6fP8/s2bPZtm0bubm5pKWl3bf4vbyq3PFrQkNv7saamnqCsoWojPfERx1QVa+OS7lyzg7lkSeXeIoolUZDsTGfkHf4MGmLFzs7HCHE36SlpeHl5YWPjw8uLi6kpaWRkZGBq6srJUuW5MiRI5QpU4ZLly/z5quvMmnyZMxmMyEhIURGRqLRaKhZsyaurq7k5eUxfPhwjEYjgwcPJisri7JlyxISEuLsw3yo5B44gPXwEYJ7v+TsUIoESVCKMNeqVXGpWpXLH4/k4ocjsFxdx0II4Xw+Pj4YjUZiYmJITk4mLS2NP5YuRcnKIi8llbqVKzP0tdeIKlmSXfv3Ex4ejru7O1qtltDQULQaDefOnSM4OJj4+Hjee+89nnjiCVatWsXjjz+Ot7c3UVFRzj7M/2S3250dgsOVWbNRBQdLafEDIglKERcxby7BQ4dgXLGCk080J+VqmZ4Qwrmu9f+Ijo4G8vt3TJw8GZVez/Ezp1m7eTOdnn4ak6Kweft22nZoi8WaR7vHXRk+bChWm43KlStjtVqZP38+bdq0ITY2lieffJIrV67wwQcfoC7kK5xbLBbHHBBns165QvaqVQT07CGlxQ9I4f7XKe47lV6PX8+eRK5bi2+XziSNHcflMZ9iOn3a2aEJUaT9vf/HpUuXaN2wET/+/DMr1q7FKyCAjm+8gWdAAAcPHuT9Ne/z5YIvee+LhQwb9CpzPv6YzMxMhg0bxvjx41myZAnr168nLS2NHTt23LfJsQXFbrfz119/ERkZecPjd7qyMcC5c+cwGAwcOpS/onvfvn2JiYmhTp06rF69+rbiOdGgIVit+HaQVYsfFOmDIhwURSH5m0mkzp2L3WjEtVo1Age+iXudOs4OTQgBZG3ajEeD+rfclpybTLwxnupmK7j4kHX0Mh4NGjzgCAvO7t27iYyMxPu6Ut79+/czduxY5s2bx+jRoyldujRdunQBYNmyZcTFxTFmzBjmzp3L+fPnGTZsGAD9+/fnr7/+YuLEiVSuXJnTp09TunRp0tLSePLJJ9m5c+e/xmI+d45TT+b3dKl49K/7dMQPP+mDIu4blUpF4IDXKLtpI8W/nABqNfG9XuRUq6e4MOB1MpYvv6lMTwjxIP3z71+AawDVg6tDWC0ILAcUjksjd+uxxx4jJSWFU6dOOf7u3OnKxgBnzpxBpVIRHh7ueG7p0qUBMBgMt3UJKW3ZTwCU2xVXAEcmbpeUGYubqA0GvFq2xPPJJ8n8809ydu8h78gREt8djGK14dP+WWeHKESRZDMaydq0+faerBSeyaV3Q61WU7p0aXJzc/nrr78IDg4mLS2N0NBQALy9vUlN/d9qyWXLluXIkSNERUWhKIpjVOSzzz5jyJAhjBgx4qb3GDp0KAMGDPjPWNKWL8ez/bNoPDwK5uDEbZEERfwjlUqFV8uWeLXMH9qMf+UVrkyYgNrDHY+GDVG7uDg5QiGKFu+nn3Z2CA+cq6srlSpV4uLFi+Tl5TmSkoyMDPz8/BzPmz17NvXr13esbDxy5EheeeUVACIiIm7a74wZM7BarXTv/u+rulsSElAuXsSrSZOCOyhxW+QSj7ht/r16gU5LwutvcLpNW/KOH3d2SEKIIiI0NJS2bduyfPlyzp8/f1srG+/fv5/Dhw/TsmVLVq9ezf/93/+Rl5fHmjVrWLp0KV999dV/vm92XP5lHbdCPqn4USSTZMUdM508yYU330Sl1lBywXwZ9hRCPDDvvPMOmzdvxt/fn0WLFjFo0CCmTp1KRkYGnTp1Ii8vz7Gycbnrur326tWLt99+m8qVK1OmTBl8fHzw9PTE1dWVFStW/OP7nR88hJyDByj/xx8P4vAeagX9+S0JirgreUePcq77C6jd3PDt2gXfzp3R+Pg4OywhxANis9k4deqUo5eKY62a6yadWq1WTCYTZ8+epVixYjz++OMF9v52u53jx4/j5+dHUFBQge337/5q3Bjvps0o9sHw+/Yej4pCVcXz6aefolKpePPNN2/apigKrVq1QqVS8fPPP9/L24hCyKVCBSJ+WIBb7dokfzuFE02bYVy50tlhCSEekNTUVLy8vIiMjCQyMpKyZctStmxZx/3IyEgMBgPu7u60bt26QJMTyJ9EW6FCBRRF4dixY/el46zl0iW4dBmP2rULfN/iv911ghIXF8fUqVOpWrXqLbd/+eWXhaYDoLg/DGXLUnzs50SuW4tHo4YkvPMuVyZPJnvHTmwZGc4OTwhxH9ntdlxdXf/1OZ6enthsNo4ePep4zGazkZSUVGBxBAcHExERweHDhx1lxgUlZ9duANxqVC/Q/Yrbc1dVPFlZWXTr1o1p06YxatSom7bv27eP8ePHs2vXLkdJmHh0af39Kf7551weM4aUqd+RbPoaALW3Nxofb3SBQXi2aIF3u7ao3d2lTbQQD4HBgwezdetWIiIimDFjBrqrK57n5ubSsWNHkpOTMRgMLFq0iODgYMaPH8+yZcvw8PBg1qxZhIaGcvToUd566y1sNhtmsxm9Xo+vry8TJkzAaDQCkJeXx+uvv05WVhZarZYvvviCgIAA5s2bx08/5fcf6dOnDy1btmTx4sUsW7YMi8VCnTp1ePvttx3xurq6sn37durWrYubm1uBnIOsHTtQlSiB9urkW/Fg3dUISv/+/Xn66ad54oknbtqWk5ND165dmTRp0m2tlGkymTAajTfcxMNHpdMR8sEHlN+9i9K//UqxsWPxf7k3nk88gdrbm8uffsrxWrU5Vr0G5158kZRZs7Cbzc4OWwhxC/v37ychIYFNmzZRoUIFlixZ4ti2YsUKKleuzPLly3nqqaeYPn06ly5d4vfff2fz5s2MHDmSkSNHAvD5558zZ84cpk+fTlJSEsuXL6dx48YcPHjQcRno+PHj1K5dm507d/Lqq6+ybt06IiMjWbx4Mbt372bbtm3MnDmTyMhI3nrrLeLi4ti3bx9Hjx7FxcXlhktKzZo1K7DkRFEUjL/+iketgr00JW7fHScoCxcuZM+ePYwZM+aW2wcOHEhMTAxt27a9rf2NGTMGb29vxy0sLOxOQxKFiEqrxRAZiXfrZwjo04fgd94hbNI3RK5fR+jo0QS+/jpqvYGk8V9wcegwZ4crhLiF2+nYGhgYiNFoJCAggHPnzhEVFYVKpaJ69eps2rQJgEqVKpGens6mTZvw8fHBzc3ttjvAXmvSlpmZic/VCfh6vR7In3zr6+t7Qx+Ugpa7axfk5eFZt+59ew/x7+7oEs/58+d54403WL16NS63aNK1fPly1q1bx969e297n0OHDmXQoEGO+0ajUZKUR5AuOBifDu0B8O/9EulLl3LxvfcxlI0k4P/+z8nRCSGu928dW61JiezZsYNyZcpgtliYNvFLks+dYf2aNaz7dTmHdv9F0sXLHIndR3TJyrR9pg15pjxqRT9O4r6zJCUkcOavUxyJ3Ze/P1MecVt2EBlRBgWFhd/O40jsPqqXrUrZ0pHY7DZGvvOh4/nTFsxg8a9LiXm8LvG7T6BSq1Gr1ajUKlQqNUGlQ/EM8P77Id2x7Lhd4OaG11NP3fO+xN25oxGU3bt3k5SURPXq1dFqtWi1WjZs2MDEiRPRarWsXr2aU6dO4ePj49gO0KFDBxo3bnzLfRoMBry8vG64iUefd/v2BPTvz5UvvyLh7XewyaU9IQoNHx8fx+X2v3ds/WP9Bp5o1Yrjp04x7osvWLNtB0937My7Q4fy0bjx7N27h8rVqlCpcTRfz/uWLdu3MnrMJyRnpUKQnqAKxSlVsQyVGkdTqXE0O8/s48nWrTh59hSfjfucJbG/UKJ6aX5e+xtn4s9y8vQppi2eRcVG1ajUOJoJ303k3IV4chUTKaosStcoT8lqZShRqRTFyodx7sCpAjkHGdu2oa9WTebMOdEdJSjNmjXj4MGD7Nu3z3GrWbMm3bp1Y9++fbz33nscOHDghu0AEyZMYObMmfcjfvGQUqlUBLzWn2KffUrWhg2c7dgJ05kzzg5LCAHExMSwZs0agFt0bOWmjq0APXr0YMOGDTwZ09jxhVSlUuHr60u9evXIyMjAaDTeVgdYtVqNq6srLi4uuLu7YzabURQFk8kEgEajwcPDA08vT3QuevRuLrh4uuLq7Y66AIpHFasVy6FDeMtK7k51R5d4PD09qVy58g2Pubu74+/v73j8VhNjw8PDKVWq1D2EKR5FKpUK77Ztca1WjfP9+nOuazfKrF6NxsPd2aEJUaRFR0cTHBxMgwYNCA8P5+2336Zv375MnTqVNq1aMnj0GJYsWeLo2ArQuXNnkpKS8Ne4MfuXHwH44IMPaNWqFTqdDnd3d955552b9te1a1c6dep0w/48PDxo3749devWxW63079/f9RqNWPGjCE2Nhar1UqTJk3+sc3Fvco7egxyc3F7XNrbO5MsFiicTh8RQdjkSZxq9RRXvvyS4GFDUallmSgh7qf/KiM2Go3o9Xq++OIL9Ho95cqVo169emgUO4uWLnPMUbHb7VSqVIl+/frx2muvcXbFTkclTYsWLRyTbf9u6tSpQP4cl5W3aPL47rvv8u67797w2K1WJL4fcnbtAp0Ol799IRcP1j1/CsTGxvLll1/+43ZFUWjXrt29vo14xOkjIggeMpi0+fNJmfa9s8MR4pF2O2XEGzZsoFevXjeVEQ/q93+OMmKAH374gfDwcGccxq2p7v3LTfrWreiiolBfrRoSziEjKKLQ8OvZE8vFS1yZMIHcvXspPuEL1P/RqVIIcef+XkY8c+ZMunTpAuSX/cbGxgL51Tx2u53NmzdTokQJTp06hZ+rC+tWr+Lsvt3YbDZmfz+NJxo1JOVCPGf37cZqzCPveFqBxarYFVQq4DY7k5usJk6ePJn/2rtYak5RFKz79hFw9XwI55EERRQqQYPfxbX6YyS+8y6ps2YR8Oqrzg5JiEfOv5URly1bliNHjhAVFYWiKCxYsIASJUowfvx4wsLC2LhxI5k5uURE12DOnDn0fLkPFouFrKwsIqJrQLSTDuqqx8rd27o5ptOnOW004lZT5p84myQoolBRqVR4tWhB5qrVpMychXv9BrhWkevAQhSkfyojHjx4MEuXLsXNzY19+/bxyy+/MHnyZL766ivy8vIIDg7G1dWVyMhIbDYbn376qWNf10Zg+vbty7FjxwDYsWMHiYmJ+Pr6OudA70LOrl2gVuP62GPODqXIk5mIolAKfm8Y+lIRxPfsSfb2Hc4OR4hHyq3KiPfv38+FCxdo06YNAQEBfPHFF5hMJrKyslixYgUtW7YkPT2ddu3aYTAYOHDgABcuXMDLy4u8vDzGjx/Pzp07mTp1KrGxscyaNYu6des+VMkJQO7u3bhUrCjVhIWAJCii0Bk8eDBN2rRhuFqNtmpVLvTvj91kIjc3l9atW9OoUSOaNWvG5cuXARg/fjz16tXjySef5OLFi0D+Gk99+/aladOmt73sghBFxfVlxIcPH6ZDhw689tprNG/enB49epCXl8fEiRP59ttvGTFiBJGRkSxdupSmTZuydetW2rVrh9lspmfPnqxcuZKhQ4fi4uJCrVq1HO+xePFinn/+eSce5d3JiduFW80azg5DIAmKKGSury6oWLkymytHYc/OJnff/v+sLrh+kbKvv/6ap556inXr1vHLL784+aiEKHzGjh3Lpk2bmD9/Pnq9nlatWuHl5YWHhwdz5syhUaNGbN68mXLlylG2bFnCw8O5fPky2dnZ9OrVizJlyrBr1y5MJhPFixdH9bdJrMuWLaNDhw5OOrq7Y7l4EUtiIq41JEEpDCRBEYXK36sLdp45gzY4mIxlS2+5qNg/LVK2cuVKNm/eTOPGjR39FoQQ/8zHx4fMzEwuX77M0aNH0el0nDt3jnPnzjH+07FUiijPLzN+pF+3Pox4+308s/W83LEXzRs147cff6FcqbKYE7KwGU2cPXsWNzc3goKCnH1YdyRn124A3CRBKRQkQRGFSlpammM9Jm9vb9LS0vDt1g3jipWUMFsc1QVTpkyhYY2W6Ew+bNm0neN7E1n6w3JHNcL58+epVasWa9asYcGCBVy4cMGZhyVEoRcTE8PatWupWbMme/bsoVmzZoSGhuKveOKao6V0hTKERZWhWkwNMm256ILdePHV3sSuW0+71m1pEF0XW1oeecfSWLJkycN5eWf3LvSlS6P193d2KAKp4hGFzK2qC/y6dSVz9Rq+at+BSk2e4bO3v2fzntV8MmY0M36YzJuDBtDnzS6UCatAhQoVHPtp2rQpWq2WmJgYjh07RokSJZx5aEIUatfmpTzxxBOEh4czZMgQBgwYwJevf0LvIa/SuVsXfv5tuaMdvdpF62hvX8InhG9nTsXg4oZKrWLZG8seykurubt3y+hJIaJS7qaTzX1kNBrx9vYmIyNDVjYugvbt28cXX3zBnDlzaNiwIUlJSTz++ON82akzE3v3RtupFx45jZj429tkW5OpUq0yCxYsIDg4mBc6vciGbWupWLEiJUuWpFOnTmzbto0vvviCEiVK0KdPHwYMGODsQxTioaHYFfKOpmKI9EGlUqHSqTHFG7FnW1Bp1CiKgtbHgPlCFu41gp0d7j2xpqVxom4MxT77FG+ZWH9XCvrzW0ZQRKFy7VvcY489Rnp6OseOHaNevXrM/3IibaJr8N7xfcSf+5UcWyof9pmCV6UsmjdvjqenJyeOn+TM2dMcPnyYyZMnM27cONLT03n99dd5//33qVq1Kv369UMjy6cLcVtUahXWlDyydx1DsdgIeLEy9hwrrhX/dwnEmpKLS6SP84IsILl79gDgWkMatBUWkqCIQmfs2LF8++23uLu7o9frmTJlCl+0a89zvV9i5YgPOXDgAN9//z2vjm7HN998zWuvvUa1atX4duL3uLu7U716dXbv3s3Bgwcd+8zOzqZMmTKSnAhxhzwbFMezQXGytiWSezgZra/LDdu1/o/GchQ5u/egDQlBV7yYs0MRV0mCIgql61txGxITSTemc8irOJv2XMCc58r6HXspWbYCiqIwes6vbMnKZdvhvfyZeIV9mzdxMTmF9Sn5c1kmDhvM1uU/8/prrznzkIR4qHnUfbQ/uE0nTuBSqdJN5dLCeWQOiig0rl/+vVatWnh7e9OjRw9+fO55Bv7+G5GPP45Wp+PJJ58kIyODLVu2cOXKFVJTUwkJCWHgwIF8//335OTkcPr0aapWrcrKlSsxa3UcTEnnjaef5Ndff3UkPkIIcc3J5i3wbN6c4HffcXYoD62C/vyWMmNRKPx9+fesrCxHK+7ZO3ZQsXhxNmzcSK9evdiyZQuhoaHExsYyadIkKlSoQLt27ejRowevv/46derUYeDAgcTGxqJSqVidnEG0vw9ubm64uLj8RyRCiKLGbjZjSUhAXyrC2aGI68glHlEo3Gr592utuFUZ6ZStUYO+ffsSFRVF7foN+e2331iyZAk2mw2NRkPHjh3p3LkzsbGxBAQE4Ofnh81mY++58ySePMF4m5Xu3bs/dOuCCCHuP0t8PNjtGCIinB2KuI6MoIhC4e8N2lJTUxk7diwb166ltFbLkp07WbRoEeO++oZf9FX4cv4SVqxYgbu7O7t37+a1117jq6++olq1akRGRpKSncP4rybyWJnSxG3aSIsWLfj2229p3Lgxb731lpOPVghRmJjOnAFAX6qUkyMR15MRFFEo/NPy7wf++os9VivdS4QR0LUrKw+eov6F1Ww8X5Htq37ntNHESwPeoGbF8kyfPh0fHx/i4uIYtn4r6T8tYuKELxjx/nsAjBkzhmeeecZpxyiEKJysly6j0unQSAfZQkVGUEShcKvl3yH/0k+lSpXwMRqxlqlG5pWLZBqNvBIdxpstYzAlnMH/8Sbs3/0nuTkXKBnuhVarUP5cLMtWLEVlUJP615+Qnczw4cNp1KgR69atc+ahCiEKGVtGBhofH6ngKWRkBEUUCtcv/x4eHs7bb79N3759KVmyJLU9vVlqt2P5+hOSz5zh7Wnf/H97dx7eVJk2fvx7kiZpWpp0b6ltylKWsi/6qhREtoIob3VQUYQRRUXFUWfUAeR9xREUFGf8uYtjFfAd1BFcGBUQXJBNZStLEUSltKyF0qZ70yTP74+WSKFKoctJy/25rnMlOVvu5yYkd59zznMAiI6O5lheHm/89V7M3nIytmWiaRqrvhjA0GG3YjQaWbx4MeHJw7l/TAmPz32Jo0ePMnToUDZv3ozZbNa51UIIf1BVoNj1DkOcRgoU4Tfmzp1b4/W8efN45ZVXqAhpxetR0eSOuYn3f/6Jo6tfp6Iwjfc/XMqEm69j1BVJ5KqLmDlzJtOnT8fj8VBQUIDL5WLw4MFcddVVhNurzm+JiYkhOTmZAwcO0K5dOz2aKYTwM56CAgx2KVD8jRziEX6tX79+bLEG0mrkSD6a/RSJv+wiceA42iU6iAi106Z9Z4bc8AiJjvY4nU4MBgNWq5XAwECCg4NxuVwopSgsLgagtLSU3bt3y1goQggfj7MAY2io3mGI00gPivBrvXr1IjYujls2rCM6IQHnd9v4r/6pDBs+nOeee45x48bx2fLPqSjJI7BVJCOvSuX4sWNcfPHFaJpGRUUFgwcPZteODErKK+nWrRvjxo0jNTUVTdOYPHkyY8aM0buZQggdeZxOLElJeochTiMFivB7Jw/9fPPkU8x9+mkO7NnD06+/zsqVK1m+fDkAb98Vx9bKaP52/00s+s96Dhw+zsMTh6MpLwFhHVnw9rvsrwjhb0/MYfQfJvDWW2/Rtm1bhgwZQlpamgzgJsQFzFMgPSj+SA7xiGZBud18+dqrDLn0YgKiohgxYgTr1q3zLW936TBKyyrxlBymPOQSEnrfiP3iRwjqeiuG8I78Z2Mh48b9CWfBJo4cOUhSUhJGo5H4+Hh27typY8uEEHrzFjgx2kP1DkOcRnpQRPNgNFJkDiBe0/C4CnyDuQF4PRUk9biYHxfu4fIJC9BMIXy/cTMAAUGxFLsCOXo0l27devLzL18SG9uKZcv+QYcOkaxZ8xnjxt2oZ8uEEDpSXi+ewkKMcpKs35ECRTQLmqbResgwjv/nEw7c2p+coZfSqnIfhRufBs3E4tVOLuvh4K8zB/L5oR7MnDmTp59+GoCPP/6YtLQ0ANq3+zMvvJDG5MmTqXAdokePflx0kYweKcSFyltYCErJIR4/JId4RLMx5J572BIXR+lWD2t+DOOKkeOxXTIF28V/wRAcS1ynKwi94mmiYy/C6XT6tnv//fe58cZfe0natWvHJ58s4dVX78FgMNK1a1c9miOE8AOe6u8K6UHxP9KDIpqN3r17E9e5M+M+/IAOu/cydeZTTJo0iXnz5jF27FjGjBnju4Fgeno6UDVs/pEjR+jcubNvP/Pnz2f+/LeAMubOfRlwAzJomxAXIk9BAQDGsFBd4xBn0pRSSu8gTlVYWIjdbsfpdPpuHifEScVr15Fzxx20/WAJgV261GtfBQWbcLuLKCzcTtu298sw10JcgIq/+YacuyaR9PVXmGJj9Q6nWWvo3285xCOajcrcXA4+8hdMndpjSU6u9/6MRitoGpFRQ6Q4EeICJYd4/JcUKKLZcP3yC978QsKuH1PvgsLrraC4eA+REVdiC+nWQBEKIZobT34BmsWCwWrVOxRxGilQRLMRdMkltLpqKLlPP03xmrX12pfBYMFkCsPPjnAKIZqYx+mU3hM/JSfJimZDMxqJf+YfZN87iQN/vp+I92ZhCG113vvzeMsAL2BssBiFEM2LFCj+SwoU0axoJhOtH/sbPw9LJXB/ICHtr9A7JCFEMybD3PsvOcQjmp2AiAgwGKg8fFjvUIQQzZzH6cQYKj0o/kgKFNHs5D73/0DTaDXwSr1DEUI0cx6nE4Mc4vFLUqCIZkUphXPJEmxXXYU5/iK9wxFCNHPGVsF4i4r1DkPUQgoU0axomoZ99GgKP/mEvQOvJPfZZ6nYu1fvsIQQzZTJ4cCVna13GKIWUqCIZifmr49w0fPPEzJsGPnvL+aXUf/NL9f9Aeenn6I8Hr3DE0I0I2ZHIpX798uQA35IChTR7GgmE7bhqcT+z3Q6rPmG+FdeJiAqkkMPPcwvaWmUbtqkd4hCiGbCnOjAW1qKJy9P71DEaaRAEc2awWwmZPBgHK+/Tpv33sVos5N91yTKd+3SOzQhRDNgdjgA5DCPH5ICRbQY1p49cbzxTyzt2pFz9z1UHj2qd0hCCD9nSkgAwLVfChR/IwWKaFEMQUEkvPoKGAwcmjpV73CEEH7OEBhIQGwsruz9eociTiMFimhxAqKiCP/jHynd8C25/3hO73CEEH7O7HBQKT0ofkcKFNEi2a9Nw2C3U/jJJ3qHIoTwc+ZEudTYH0mBIlocr8tF7jNz8TqdhN9+u97hCCH8nMnhwCWXGvsduVmgaFHceXkcuO9PlGdmEjf3GeyjRukdkhDCz5kdiXiLivAUFBAQFqZ3OKKaFCiixShZv56Dj/wVDBqJby/E2rOn3iEJIZoBc2LVpcaV2dlSoPgROcQjWgRvaSmHpkzFnJBA2yVLpDgRQtSZ+eSlxnIeil+RAkW0CLnPPounuJi4ObMxRUfrHY4QohkxBAdjjIqUsVD8jBQootlzHz9O/nv/Juq+yZjbtNE7HCFEM2R2JMpYKH5GChTRrJXt2EnOpLsx2mzYr7tO73CEEM2UjIXif6RAEc2Wc+lSsm64AU9hIQlv/JOA8HC9QxJCNFMyFor/kat4RLNVtmMnprg42i9fhmY06h2OEKIZMzscePLz8RQWYrTZ9A5HID0oohkzxUTjKSyU4kQIUW8mRyIAruwcnSMRJ0mBIpqvgACUx6N3FEKIFsDsqLrUuFJOlPUbUqCIZsuTXyBdsUKIBmG02TCGhcl5KH5EChTRbHlLStBMJr3DEEK0EGaHQ8ZC8SP1KlDmzJmDpmk8+OCDvnmTJk2iffv2WK1WoqKiSEtLY/fu3fWNU4gzWJLaU3nkCMrl0jsUIS5IRW4PhS1oIiGBiv1yiMdfnPdVPBs3bmTevHn06NGjxvy+fftyyy234HA4OHHiBI8//jipqans27cPo5zMKBqQJSkJ3G4q9mUR2Kmj3uEIcUH5Iq+QVkYDAZpWr/28MON/2f79t7ROSGTGy68SUN0rWl5WxrQJf6SkqBBjQACz3niTiOgYjh48yJyH/kxpcRF9+qUw6dH/QSnFy088TubmTXg8Hl5c8hGWwMBzjiWgdRzGDRvq1R7RcM6rQCkuLuaWW27hn//8J7Nmzaqx7K677vI9b9OmDbNmzaJnz55kZWXRvn37+kUrxCkCk5PRAgMpXr1aChQhmthRVyVDWkfUax/btm3DczyXrevX8+STT/LLymXcfPPNAHzwxQpSevdk9uzZvP3222x6/10effRRbr57Bu+88ToXXXSRbz+LFy+mV2ICb/3j2XrF4+yYxKG8PDzFJRhbBddrX6L+zusQz+TJk7n66qsZOnTo765XUlLCW2+9Rdu2bUmovhnT6SoqKigsLKwxCVEXhuBgQoYOxfnxxyil9A5HiAvKlWEhfFdQXK99rF+/ntTUVABGjBjBunXrfMuSkpIoKSkBID8/n8jISCorK8nKyuKhhx5i8ODBrF+/HoClS5eSnZ3NlVdeyd/+9rfzjsfsqL6rcY6ch+IPzrlAeffdd9myZQuzZ8/+zXVeeeUVWrVqRatWrVi2bBkrV67EbDbXuu7s2bOx2+2+6bcKGSFqYxsxHNfPP+M+fFjvUIS4oMQFmrEFGFl+zInbe35/IOTn52OrvhLPbrdz4sQJ37IOHTqwa9cuunbtymuvvcbYsWM5fvw4GRkZPPPMMyxatIgHHngAgKNHjxIbG8vXX3/Nrl27+Pbbb88rHlN1gSInyvqHcypQcnJyeOCBB/jXv/5F4O8c37vlllvYunUrq1evpmPHjtx4442Ul5fXuu60adNwOp2+KSdHBskRdWfp3BmA4lP+8hJCNI3kVlauDA9hZZ7zvLYPDQ319Zo7nU7CT7ldxYIFC+jfvz+ZmZk88cQTzJw5k9DQUJKSknA4HMTGxmIymXC73YSGhjJ48GAABg8eTGZm5nnFYwwNxRsSIjcN9BPnVKBs3ryZ3Nxc+vTpQ0BAAAEBAaxevZoXXniBgIAAPNWDZtntdjp06MAVV1zB4sWL2b17Nx9++GGt+7RYLNhsthqTEHVljo8nJDWVvNfm6R2KEBekQKOBSgXe8zjM2q9fP1atWgXAihUrSElJ8S1TShEZGQlAZGQkTqcTq9VKREQEBQUFlJSUUFFRQUBAACkpKWRkZACQkZFBu3btzqstmqahxSdQuC/rvLYXDeucTpIdMmQIO3bsqDHvtttuo3PnzkyZMqXWq3SUUiilqKioqF+kQvwGY1gYhlat9A5DiAtWpCmACq/Cajy3K3p69epFTEwMAwYMwOFw8PDDDzNp0iTmzZvH2LFjGTNmDIsXL8bj8ZCeng7AU089xahRo3C5XL7zTSZOnMiECRNYuHAhnTp1YtCgQefdluA2iRRmSQ+KPzinAiUkJIRu3brVmBccHExERATdunXjl19+4b333iM1NZWoqCgOHDjAnDlzsFqtjBw5skEDF+KkgIhw3EeOoJRCq+clj0KIc2cxaLi8XqzGc7/uYu7cuTVez5tX1Rtqt9tZvnz5Gev369ePNWvW1JgXHBzM+++/f87vXRtrm0SKNm1qkH2J+mnQkWQDAwNZs2YNI0eOJCkpiTFjxhASEsL69euJjo5uyLcSwicgJhaP8/yOgQsh6q9dkIWfS1tGL7nZkYjx2DG8ZWV6h3LBO++B2k76+uuvfc/j4uL47LPP6rtLIc6J0W4HIPvWCYRPuJWQ6pPlhBBNo9zrpdjj1TuMBmFOrL6SJyeHwI4yvpKe5F48otkLGZ5K3NNzUF4PB+6dzJGZs2RcFCGayN6Scj4/XsgV4SF6h9IgfGOhyE0DdScFimj2NE3DnpZG4ttvE/WXv5D/r3/hkrPwhWgwU6ZMYcCAAYwfP57Kykrf68Gjr2f0f4/izbGjGTJkCFlZWYwaNYrLLruMqKgoUlJSmDFjBgcOHGDQoEHY7XYSExO59dZb/faPCGNEBF5rkIyF4gekQBEthqZphAypOrzj2p+lbzBCtBDbtm3j4MGDvpNT4+PjeeuttwiOi6fU7SHIaCQ4JIScnBw6derEli1b2LdvH5dddhn79+/n448/pkePHuzevZv+/fvjcDhYsWIFM2fO1LtptdI0DZUQT0lWlt6hXPDqfQ6KEP7E3KYNxogIStasJaQelxoKIaqsX7+ezp07s3v3bmbPns2dd97J+PHjiY27iAOHD7Pi00+IS/sDPcIjcXkVoWGh5J84wbLly0lo0xaLzUZZWRkXtWnD16tXM+fV1/j0gyUsXrqUG+57oMZN/U69Bu/UC/J+79q8mttotc//3W1OnV/1QktIoEguNdad9KCIFkUzGtFMJgqWLKF8zx69wxGi2cvPzycnJ4fU1FR+/PFHWrVqxf79+7lm5FUU55+gtKSEr/+1kI1rvyEiIgJnfj7Zv/yCQdN4ecECtm/aRHl5OcqrcFVU8Midd2Axm2nfoQM/7crEqIFRA4NWVSycLBiUqpq8CjzVk1upGlOlUriqpwqvotzjpdzjpczjpfSUqfiUqcjjobB6cro9FFT+OuVXusmvdGNIcGA6eEDfxAvpQREtT8yj0zh4/wMUf72awE6d9A5HiGYtNDSUrVu3YrPZyM3NJSQkhPz8fOx2O9u3b6dNmzaEhobyww8/kLFpI2FhYVgsFm6++WZuGDaU8rIy4uLiSGqTSNZPe2nbti0xdjv/+c9/uPf220m0WvRu4hkKktpxeMFhvC4Xht+4j5xofFKgiBbHlprKiT59KP/hB71DEaJZu/TSS8nMzKSyspJhw4aRk5NDYmIiWVlZdO/enfLycjRN44477qBtl64snPcaJ06cQCnF/PnzSU5OZu/evRw5coRPP/0Uk8nE/v37KS8vp3Xr1sTGxurdxFqZHA5QisoDB7Cc57D5ov7kEI9okYIuuYTS77/32ysFhPB3aWlpbNu2jeuuu47w8HAefPBB3G43R48eZd++fZSXl2Oz2TCbzaSnp/P2ggW4XC4waFiDrADs3191HofJZMJsNlNWVobD4aBz587ExcXRtWtXPZv4m8yJiQC49st5KHqSAkW0SNaePfGcOEHFjz/qHYoQzcaUKVOIj48nPDycL774gnHjxtG5c2dGjRqF2+1m5cqVvjsFG41Ghg0bRkVFBV6vF1dR1V2JzQFGKsqrRpUtLy8nMDAQt9tNXFwcNpuNqKgoysvLefzxxzEY/PMnKCAqCi0wUMZC0Zl/fjqEqKegPr0BODz9f3SORIjmYdu2bezcuZMrr7yS/v37YzAYOHbsGCNGjCAjI4OAgAAefvhhZs6cid1ux2AwsGTJEoxGI7179yYmJhqbzUplpRuv10toaCiRkZGYTCZsNhv5+fl0796dr776ipUrV3LxxRfr3eTfpBkMmBMSZCwUnUmBIlokY2go5sREynfuJG/+fDwFBXqHJIRfW79+PZGRkaSmppKQkIDZbOann37CbrdTUVGB2WympKQEi8VCUVERXq+XsLAwRo8eTU5ODrm5x7BabYSGhgFQVFSEwWDA7XbTvXt3Fi1axE8//cThw4d1bmndmBIduKQHRVdSoIgWy/FmOsbISHLnPE3O5Ptw5eToHZIQfis/Px+Px4PNZiM6Oprg4GD279+P0+kkLy+P4OBg3nzzTaZOnQpATEwMkZGRbNy4EY/HA0BpaSlDhw5F0zS8Xi+RkZGUlpayfft2pk+fTkFBQY37t/kzsyNRChSdSYEiWizTRRfR4asviX/pRcozM/l5WCpZt4yj8uBBvUMTwu+EhoZiNBopLCykQ4cOaJpGUFAQKSkpFBQU0L17d6xWK7fddhvt27fn6NGj/PTTT+zbt4/Y4DBMJhPx8fG8//77AFitVrKzswkPD0fTNMrKyujfvz8333yzzi2tG7PDQeXBg6jKSr1DuWBJgSJaNM1kImToUNqvWEHcs8/iPnqU/bdOoOjLL1HelnH3VSEaQr9+/cjLy2PVqlVkZWURGBhIeHg4o0ePZvbs2ezdu5cZ06eR1tHIibzjREdF4ejSm+v/dxE5XhOtwqKJjY0lKiqK7OxsSkpK6N69OxUVFZhMJmJjY/n888/1bmadmRMd4PFQeeiQ3qFcsGQcFHFBMMVEY7/maoL69ObgQw9z4N7JmBwOrD16oFwubCOG02rQIAxWq96hCqGLXr16kZyczDvvvMPy5csZMmQIwcHBGI1G/vjHP/LRRx/x2BOz8LjKSEqI5faRM1lz8BMOff0qlorjZGdnExQUxOeff84VV1xBXl4erVq1YtWqVVxyySV6N++cnbyrsSs723fZsWhamvKzgSIKCwux2+04nU5sNpve4YgWqiwjg/zFi3Hty0K5Kynfth2MRixJSQR274a1W3cCu3cjsGNHNJNJ73CF8B/H9uAtOsZRV3tad26tdzSNRnk87OnVm+gpUwgfd4ve4TQLDf37LT0o4oJk7dULa69evtcVv+yjdONGynfuoGzHTpwffgQeD5rZjCW5M9auXbF07ERgl2QsnTvL8NfiwhXVCUNUJ1puaVJFMxoxxcfjypbB2vQiBYoQgKVdWyzt2sKYGwHwlpVR/sNuX8FS8t335L/376qixWTC0iWZ4P+6lLBx4zDFROscvRCiMZgcCVRmy9V/epECRYhaGKxWgvr09g34BuCtqKBizx7KMrZRtn07+e+9R8GHH9J+2WcYQ0J0jFYI0RjMCQ5KNmzQO4wLllzFI0QdGSwWrD16EP7H8Vz07FzafrAEb1ERef98Q+/QhBCNwOxIoDInR67404kUKEKcJ3N8PGFjx1Lw3nuo6oGqhPgtU6ZMYcCAAYwfP57KU8bWKCsrY9SoUQwcOJAhQ4Zw9OhRAP7+97+TkpLC8OHDfaOvfvXVV1x++eUMGDCAb775Rpd2XEhMCQkolwt3bq7eoVyQpEARoh5CUofhcTopWLxE71CEH9u2bRsHDx5kzZo1dO7cmcWLF/uWLVu2jG7durF69WomTJhAeno6R44c4dNPP2Xt2rXMnDmTmTNnAvDoo4+ybNkyli9fzmOPPaZXcy4Yp15qLJqeFChC1ENQ796E3nA9R2bMIC/9Tb3DEX5q/fr1pKamAjBixAjWrVvnW5aUlERJSQlQNdx8ZGQk+/fvp2vXrmiaRp8+fVizZg0Abreb0NBQgoODcbvdHD9+vOkbcwExxceDplEpt8nQhZwkK0Q9xT7xBGgGjr34Iva0/yYgMlLvkISfyc/Pp3Xrqgtz7XY7J06c8C3r0KEDu3btomvXriil+P777ykvL2fTpk1UVFTw1Vdf+da3WCxkZ2djsVjYuXOnr6ARjcNgsRAQE4NLruTRhfSgCFFPmqYROXkyxpAQDtz3J/xs7EPhB0JDQyksLATA6XQSHh7uW7ZgwQL69+/P65+/ztgHxjLpr5PYU7GHITcN4bKBl5H+fjqxbWPZfHQzzz//PLfddhuTJ0+me/fuxMbG6tWkC4Y5IYHKHDnEowcpUIRoAKaYaGKf+BtlGRmU79ypdzjCz/Tr149Vq1YBsGLFCi69/FImrpjIU989xZoDaygIKCDYFEz/Tv0J8YaQclEKsx6YxdZvtzJ5/GSuHX4t5e5y+vbtyxdffMFrr71G69atCZHL2xudyZEgPSg6kUM8QjSQ4H79ADhw72Si/vxnMGhoBgNoGmgGNIMGBgOg/brs9NeaAVCgqibl9YKiap7XW9U7o1TVPHXKpY8ne21O6b052ZPjKSjAaA9FMxrAYKx6L6MRzWgEgxEtwAhGI1pAwK/zjIaqeYbaHzEYqtc1/LqNQTtzXQFU3ecmJiaGAQMG4HA4ePjhh3njlje4ds61pNyawuw/zebBVQ/i8XhIT08H4KabbiI3N5fExERefvlltuZvZc6cOaxYsYKgoCBefPFFnVt1YTAnOCha9YXeYVyQ5F48QjSQytxcfrpioN5h+JffK3KMBjTD7z0afUXcGfs4WWz93j60U9bVDDXnndzvyWVaVZHo+vkXilauJPSG6zE5HGgBJjTT6VNAjdcYjOD1oDzeqke3B5S3urg8WUwqahSeSvHjiT10DO14xnzf+qdso5Ti5/yfaG9vf8Z8Kiur/sI3VBfBVBfCmgYa1YXiyXmAplXlQTutMDZW5/lksXlKMYtmqJnHGuudWohqpzzVap196nzt1HVOU+tPU63zat24tpl12NeZ80o3bSZ/0SI6fvctRru91lhFFbkXjxB+yhQdTecfdlV9yXm9NXs8vF6Ut/qHpdZl1T0lXk/1D4tW/UNS/fzkDw7VX+q+H6Aa3/zVy0+dpfm+dJVS4PFUvZfXWzV2i8dT9eh2ozzVP64e968/tr/56Pl1H75HdZZtatmHx4vy1uHRe0rstT26XKhTtvHl1OP5td3KW73MU/VvcXL7k/9eSuHOywPAufQ/aIGBqMpKqKysemxgrYBD57B+MHDklM+G7zMCqMpKzEntaxY3Xi+K6oKnun1KeX9dx+v99fXJf8dTP5unzav9R//C4PssiCYlBYoQDahGQUHNPxxF8+UrcqqLFeV2+57jdlf31Bh/fawuILVaCgrQqh9qLzZqnf87PQ1NRZ1aePuKXC++nolTC5g6PK9L532t7W7oebX8Lz1jNZNJbhCqAylQhBDiLDRNg4AAtIAAsFr1DkcXmqZVH5ozSuEtmoScxSaEEEIIvyMFihBCCCH8jhQoQgghhPA7UqAIIYQQwu9IgSKEEEIIvyMFihBCCCH8jhQoQgghhPA7UqAIIYQQwu9IgSKEEEIIvyMFihBCCCH8jhQoQgghhPA7UqAIIYQQwu9IgSKEEEIIv+N3dzM+eQvuwsJCnSMRQgghRF2d/N0++TteX35XoBQVFQGQkJCgcyRCCCGEOFdFRUXY7fZ670dTDVXqNBCv18uhQ4cICQlB07Rz2rawsJCEhARycnKw2WyNFGHzIfmoSfJRk+SjJslHTZKPmiQfNdWWD6UURUVFxMXFYTDU/wwSv+tBMRgMxMfH12sfNptNPkCnkHzUJPmoSfJRk+SjJslHTZKPmk7PR0P0nJwkJ8kKIYQQwu9IgSKEEEIIv9OiChSLxcKMGTOwWCx6h+IXJB81ST5qknzUJPmoSfJRk+SjpqbIh9+dJCuEEEII0aJ6UIQQQgjRMkiBIoQQQgi/IwWKEEIIIfyOFChCCCGE8DstpkDZsmULw4YNIzQ0lIiICO666y6Ki4t9y/Py8hgxYgRxcXFYLBYSEhK47777Wuw9f86Wj23btnHzzTeTkJCA1WolOTmZ559/XseIG9fZ8gFw//3307dvXywWC7169dIn0CZSl3xkZ2dz9dVXExQURHR0NI888ghut1uniBvXjz/+SFpaGpGRkdhsNvr3789XX31VY50vvviCfv36ERISQmxsLFOmTLmg87Fx40aGDBlCaGgoYWFhDB8+nG3btukUceM6Wz7mz5+Ppmm1Trm5uTpG3jjq8vmAqrz06NGDwMBAoqOjmTx58jm9T4soUA4dOsTQoUNJSkriu+++Y/ny5WRmZjJhwgTfOgaDgbS0NJYuXcqPP/7I/PnzWbVqFXfffbd+gTeSuuRj8+bNREdH83//939kZmYyffp0pk2bxksvvaRf4I2kLvk46fbbb2fMmDFNH2QTqks+PB4PV199NS6Xi/Xr17NgwQLmz5/PY489pl/gjeiaa67B7Xbz5ZdfsnnzZnr27Mk111zDkSNHgKqCfuTIkYwYMYKtW7fy3nvvsXTpUqZOnapz5I3jbPkoLi5mxIgROBwOvvvuO9auXUtISAjDhw+nsrJS5+gb3tnyMWbMGA4fPlxjGj58OAMHDiQ6Olrn6Bve2fIB8I9//IPp06czdepUMjMzWbVqFcOHDz+3N1ItwLx581R0dLTyeDy+edu3b1eA2rt3729u9/zzz6v4+PimCLFJnW8+7r33XjVo0KCmCLFJnWs+ZsyYoXr27NmEETatuuTjs88+UwaDQR05csS3zquvvqpsNpuqqKho8pgb07FjxxSgvvnmG9+8wsJCBaiVK1cqpZSaNm2auvjii2tst3TpUhUYGKgKCwubNN7GVpd8bNy4UQEqOzvbt05dvmOao7rk43S5ubnKZDKphQsXNlWYTaYu+Thx4oSyWq1q1apV9XqvFtGDUlFRgdlsrnFzIqvVCsDatWtr3ebQoUN88MEHDBw4sElibErnkw8Ap9NJeHh4o8fX1M43Hy1VXfKxYcMGunfvTkxMjG+d4cOHU1hYSGZmZtMG3MgiIiLo1KkTCxcupKSkBLfbzbx584iOjqZv375AVc4CAwNrbGe1WikvL2fz5s16hN1o6pKPTp06ERERQXp6Oi6Xi7KyMtLT00lOTqZNmzb6NqCB1SUfp1u4cCFBQUFcf/31TRxt46tLPlauXInX6+XgwYMkJycTHx/PjTfeSE5Ozrm9Wb3KGz+xc+dOFRAQoJ555hlVUVGhTpw4oUaPHq0A9dRTT9VY96abblJWq1UBatSoUaqsrEynqBvPueTjpHXr1qmAgAC1YsWKJo628Z1rPlp6D0pd8nHnnXeq1NTUGtuVlJQoQH322Wd6hN2ocnJyVN++fZWmacpoNKrWrVurLVu2+JavWLFCGQwGtWjRIuV2u9WBAwfUgAEDFKAWLVqkY+SN42z5UEqpHTt2qPbt2yuDwaAMBoPq1KmTysrK0inixlWXfJwqOTlZ3XPPPU0YYdM6Wz5mz56tTCaT6tSpk1q+fLnasGGDGjJkiOrUqdM59cD6dQ/K1KlTf/PEo5PT7t276dq1KwsWLODvf/87QUFBxMbG0rZtW2JiYs645fNzzz3Hli1b+Pjjj/n555/5y1/+olPrzl1j5ANg586dpKWlMWPGDFJTU3Vo2flprHw0V5KPmuqaD6UUkydPJjo6mjVr1vD9999z7bXXMmrUKA4fPgxAamoqc+fO5e6778ZisdCxY0dGjhwJ0Gxy1pD5KCsrY+LEiaSkpPDtt9+ybt06unXrxtVXX01ZWZnOLa2bhszHqTZs2MAPP/zAxIkTdWjV+WvIfHi9XiorK3nhhRcYPnw4l112Ge+88w579+6t9WTa3+LXQ90fO3aMvLy8312nXbt2mM1m3+ujR48SHByMpmnYbDbeffddbrjhhlq3Xbt2LQMGDODQoUO0bt26QWNvDI2Rj127djFo0CDuuOMOnnzyyUaLvTE01ufj8ccf56OPPiIjI6Mxwm40DZmPxx57jKVLl9bIwb59+2jXrh1btmyhd+/ejdWMBlPXfKxZs4bU1FTy8/Nr3Da+Q4cOTJw4scaJsEopDh8+TFhYGFlZWXTp0oXvv/+eSy65pNHa0VAaMh/p6ek8+uijHD582FeguVwuwsLCSE9P56abbmrUtjSExvh8AEycOJEtW7awdevWRom7sTRkPt566y1uv/12cnJyiI+P960TExPDrFmzuPPOO+sUU8D5NaVpREVFERUVdU7bnDxm/uabbxIYGMiwYcN+c12v1wtUHV9uDho6H5mZmQwePJhbb7212RUn0Pifj+amIfNx+eWX8+STT5Kbm+u7CmHlypXYbDa6dOnSsIE3krrmo7S0FDizJ8RgMPi+I07SNI24uDgA3nnnHRISEujTp08DRdy4GjIfpaWlGAwGNE2rsVzTtDNy5q8a4/NRXFzMv//9b2bPnt1wgTaRhsxHSkoKAHv27PEVKCdOnOD48eMkJibWPagGPTCloxdffFFt3rxZ7dmzR7300kvKarWq559/3rf8008/VW+++abasWOH2rdvn/rkk09UcnKySklJ0THqxnO2fOzYsUNFRUWpcePGqcOHD/um3NxcHaNuPGfLh1JK7d27V23dulVNmjRJdezYUW3dulVt3bq1xV21otTZ8+F2u1W3bt1UamqqysjIUMuXL1dRUVFq2rRpOkbdOI4dO6YiIiLUH/7wB5WRkaH27NmjHn74YWUymVRGRoZvvWeeeUZt375d7dy5Uz3xxBPKZDKpDz/8UL/AG0ld8vHDDz8oi8Wi7rnnHrVr1y61c+dONW7cOGW329WhQ4d0bkHDquvnQyml3njjDRUYGKjy8/P1CbYJ1DUfaWlpqmvXrmrdunVqx44d6pprrlFdunRRLperzu/VYgqU8ePHq/DwcGU2m1WPHj3OuLzryy+/VJdffrmy2+0qMDBQdejQQU2ZMqXFfpDOlo8ZM2Yo4IwpMTFRn4Ab2dnyoZRSAwcOrDUn+/bta/qAG1ld8pGVlaWuuuoqZbVaVWRkpHrooYdUZWWlDtE2vo0bN6rU1FQVHh6uQkJC1GWXXXbGycCDBg3yfX9ceumlLfJk4ZPqko/PP/9cpaSkKLvdrsLCwtTgwYPVhg0bdIq4cdUlH0opdfnll6uxY8fqEGHTqks+nE6nuv3221VoaKgKDw9X1113XY3L0uvCr89BEUIIIcSFqXmcfi6EEEKIC4oUKEIIIYTwO1KgCCGEEMLvSIEihBBCCL8jBYoQQggh/I4UKEIIIYTwO1KgCCGEEMLvSIEihBBCCL8jBYoQQggh/I4UKEIIIYTwO1KgCCGEEMLvSIEihBBCCL/z/wGo0rNk0IVseQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops.  These should all be within single digits\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[410], line -1\u001b[0m\n\u001b[0;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops.  These should all be within single digits\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if allUnits[u] in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()\n",
    "plotThis = False\n",
    "for t in popHDlist:  #seems like for TN, we lost a vtd in converting from unit-based to vtd-based\n",
    "    if abs(HDvPop[t] - HDvtdbasedPop[t]) > 12:  #10  #12\n",
    "        plotThis = True\n",
    "        print(\"t, HDunit-based pop - pop from vtd-based HDs\",t,r3(HDvPop[t] - HDvtdbasedPop[t]) )\n",
    "        plotPoly(tractCP[t].buffer(0.1))\n",
    "if plotThis:\n",
    "    plotPoly(MAP)\n",
    "    print(\"Here are the HD centers with significant pop difference between unit-based and vtd-based HD lists\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for WI\n",
      "In year/election 2020 Dem and GOP total votes were 1630058 1609642 for a stateGOP of 0.49685\n",
      "There are a total of 7059  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1382123 1404922 for a stateGOP of 0.50409\n",
      "There are a total of 7059  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for WI\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv WI7059VRApatchedVTDs.csv\n",
      "enter the number of Congr'l districts in the state; e.g. 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 7059 drawn districts.  This state has 8 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of Congr'l districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 1630058 1609642 -20416\n",
      "ensemble : 1630538 1604656 -25882\n",
      "the true and ensemble state GOP leans are 0.49685 0.49599990539845146\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 7059 7059 8\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
